How is Statistical Analysis used?
Zippia reviewed thousands of resumes to understand how statistical analysis is used in different jobs. Explore the list of common job responsibilities related to statistical analysis below:
- Conducted statistical analysis at varying levels of complexity that is used for NATO and ISAF forces.
- Performed quality checks and statistical analysis on assay generated data, to evaluate the activities of tobacco condensate and other chemicals.
- Directed an R&D transformation project of model refinement using complex statistical analysis and an innovative factorial design approach.
- Engaged in professional development activities to foster new and existing skills, including two online courses in multivariate statistical analysis.
- Collaborate with computer programmers on the development of databases and programs for statistical analysis of quality control and research data.
- Train junior level staff in statistical analysis techniques and protocol adaptation for verification and validation of Point-of-Care devices.
Are Statistical Analysis skills in demand?
Yes, statistical analysis skills are in demand today. Currently, 12,292 job openings list statistical analysis skills as a requirement. The job descriptions that most frequently include statistical analysis skills are social scientist, actuarial analyst, and research biostatistician.
How hard is it to learn Statistical Analysis?
Based on the average complexity level of the jobs that use statistical analysis the most: social scientist, actuarial analyst, and research biostatistician. The complexity level of these jobs is challenging.
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What jobs can you get with Statistical Analysis skills?
You can get a job as a social scientist, actuarial analyst, and research biostatistician with statistical analysis skills. After analyzing resumes and job postings, we identified these as the most common job titles for candidates with statistical analysis skills.
Social Scientist
Job description:
A social scientist conducts studies and spearheads research projects that focus on society and human behavior. Although the extent of their responsibilities varies upon their industry or institution of employment, it usually includes planning procedures according to research requirements, identifying and coordinating with research subjects, conducting surveys and interviews, and gathering and analyzing various data. Through the findings of their research, a social scientist generates conclusions and recommendations that will determine or support future efforts. When it comes to employment, a social scientist may work for government agencies, private companies, facilities, or even become an instructor at learning institutions.
- Statistical Analysis
- Data Collection
- Social Science Research
- Data Analysis
- Communicate Research Findings
- Quantitative Data
Actuarial Analyst
Job description:
Actuarial Analysts use statistical formulas to assess the probability and costs of certain events such as accidents, property damages, injuries, and deaths. They are usually specialized in finance, general insurance, health and care, life insurance, and savings/investment.
- Statistical Analysis
- SAS
- Statistical Data
- VBA
- Actuarial Models
- PowerPoint
Research Biostatistician
Job description:
Most of the research biostatisticians oversee clinical trials and gather data for the development of new treatment interventions. They ensure that legal, scientific protocols are followed but are concerned with the accurate gathering and evaluation of data and recording. Research biostatisticians prepare results that outline findings, information, and implications of these trials, and present them for new treatment modalities. Part of their tasks is to enforce ethical consideration in trial programs, analyze and report findings for future research, and develop standards for data collection procedures.
- SAS
- Statistical Analysis
- Study Design
- Research Projects
- Stata
- Clinical Trials
Device Processing Engineer
- Statistical Analysis
- Data Analysis
- Process Integration
- JMP
- ISO
- Process Flow
Principal Biostatistician
Job description:
Principal biostatisticians are professionals who are responsible for communicating all the activities related to biostatistics of assigned projects to the sponsor companies. These biostatisticians are required to participate in the development of finished products used in clinical trials and provide statistical oversight to ensure data integrity and appropriate analysis are performed. They must develop customized statistical programs that can generate statistical data listings, summary tables, and figures. Principal biostatisticians must also answer all the questions from regulatory authorities appropriately.
- Clinical Trials
- QC
- Statistical Analysis
- Clinical Study Reports
- Statistical Methods
- Statistical Reports
Statistical Analyst
Job description:
A statistical analyst is primarily responsible for gathering data, ensuring accuracy and relevance to the subject. Their other responsibilities also revolve around designing and implementing strategies for collecting information, analyzing data through particular software, and presenting the company's findings to decision-making officials. There are also instances when a statistical analyst must record and organize data using a database, collaborate with other teams, and even train new analysts. Furthermore, as a statistical analyst, it is essential to adhere to the company's policies and regulations.
- Statistical Analysis
- Data Analysis
- Statistical Methods
- Database
- Data Collection
- Visualization
Medical Researcher
- Patients
- Statistical Analysis
- Vital Signs
- Laboratory Practices
- Clinical Research Studies
- Research Projects
Senior Statistical Analyst
Job description:
A senior statistical analyst studies and converts data. They also manage the statistical documentation in their company, and so they work with a variety of departments. They conduct maintenance on data servers. They may also create reports, model potential trends, and analyze risks.
- Python
- Statistical Analysis
- Data Analysis
- Statistical Methods
- ISE
- QC
Research And Evaluation Manager
- Management System
- Data Analysis
- Statistical Analysis
- DOD
- Public Health
- Research Projects
Survey Statistician
Job description:
A survey statistician is responsible for collecting and reviewing data and creating reports based on survey findings. Your duties include developing statistical models that will interpret the data accurately and clearly, promoting the organizational goal to increase the participation of respondents in surveys, and guiding junior statisticians in doing statistical reviews. In addition, you will create reports on statistical analysis and insights needed by your employer. Other duties include conducting statistical analysis using SPSS software, proliferating the method that you developed across the organization, and assist with database management.
- Statistical Techniques
- Statistical Data
- Statistical Analysis
- Data Analysis
- Statistical Software
- SAS
Economist Research Assistant
- Research Projects
- R
- Macro
- Statistical Analysis
- Econometrics
- Economic Data
Director Of Institutional Research
Job description:
A director of institutional research oversees and leads the research programs of an organization or institution. They primarily take the lead in developing research plans, setting goals and standards, establishing timelines, securing fundings, organizing and managing research teams, recruiting new members of the workforce, and conducting regular assessments to ensure optimal operations. Moreover, a director of institutional research monitors all activities, addressing and solving issues should any arise. It is also their responsibility to empower research teams while implementing the organization's policies and standards.
- Data Collection
- Data Analysis
- Statistical Analysis
- SPSS
- Institutional Effectiveness
- Visualization
Division Engineer
- Technical Support
- CAD
- Capital Projects
- Construction Management
- Statistical Analysis
- Construction Projects
Human Factors Scientist
Job description:
A human factors scientist conducts analysis and research on human behavior. Depending on which industry they are in, they analyze human behavior relevant settings and apply that data. They also review technical data and scientific literature. They may serve as a technical consultant on some scientific boards or committees in the behavioral science field. They develop new methods and techniques to solve existential problems.
- Human Subjects
- Consumer Products
- Data Collection
- Statistical Analysis
- User Experience
- Data Analysis
Public Health Epidemiologist
- Health Issues
- Data Collection
- SAS
- Statistical Analysis
- Health Data
- Data Management
Epidemiologist
Job description:
An epidemiologist specializes in studying and investigating different diseases, including its causes and effects on the human body. Their responsibilities revolve around gathering samples and subjecting them to various experiments and scientific procedures, traveling to different areas to conduct observations and in-depth analysis, maintaining extensive records, coordinating with other scientists and experts, and summarizing findings into reports and presentations. Furthermore, as an epidemiologist, it is vital to utilize expertise by creating policies for a healthy and safe environment for everyone.
- SAS
- Data Analysis
- Statistical Analysis
- Patients
- Infectious Disease
- Data Management
Senior Device Engineer
- Device Design
- Product Development
- ISO
- RF
- Statistical Analysis
- Data Analysis
Quality/Reliability Engineer
Job description:
Quality training managers are business professionals who evaluate the development and growth needs of employees and their company. The managers develop, facilitate, and oversee the training programs for employees. They focus on how to translate the development for performance enhancement and productivity growth. Part of their job is to assess corporate needs and enforce plans for training and development. They understand the needs and requirements of the customers so they can develop effective processes for quality control among employees.
- Product Quality
- Statistical Analysis
- Corrective Action
- Failure Analysis
- Quality Standards
- Data Analysis
How much can you earn with Statistical Analysis skills?
You can earn up to $71,150 a year with statistical analysis skills if you become a social scientist, the highest-paying job that requires statistical analysis skills. Actuarial analysts can earn the second-highest salary among jobs that use Python, $75,593 a year.
| Job title | Average salary | Hourly rate |
|---|---|---|
| Social Scientist | $71,150 | $34 |
| Actuarial Analyst | $75,593 | $36 |
| Research Biostatistician | $88,264 | $42 |
| Device Processing Engineer | $94,372 | $45 |
| Principal Biostatistician | $120,026 | $58 |
Companies using Statistical Analysis in 2025
The top companies that look for employees with statistical analysis skills are Intel, Ryder System, and Deloitte. In the millions of job postings we reviewed, these companies mention statistical analysis skills most frequently.
| Rank | Company | % of all skills | Job openings |
|---|---|---|---|
| 1 | Intel | 10% | 365 |
| 2 | Ryder System | 9% | 6,081 |
| 3 | Deloitte | 8% | 22,962 |
| 4 | Marriott International | 7% | 6,808 |
| 5 | Johnson & Johnson | 7% | 1,728 |
Departments using Statistical Analysis
| Department | Average salary |
|---|---|
| Research & Development | $100,577 |
20 courses for Statistical Analysis skills
1. Business Statistics and Analysis
The Business Statistics and Analysis Specialization is designed to equip you with a basic understanding of business data analysis tools and techniques. Informed by our world-class Data Science master's and PhD course material, you’ll master essential spreadsheet functions, build descriptive business data measures, and develop your aptitude for data modeling. You’ll also explore basic probability concepts, including measuring and modeling uncertainty, and you’ll use various data distributions, along with the Linear Regression Model, to analyze and inform business decisions. The Specialization culminates with a Capstone Project in which you’ll apply the skills and knowledge you’ve gained to an actual business problem.\n\nTo successfully complete all course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later.\n\nTo see an overview video for this Specialization, click here!...
2. Statistical Analysis using Python Numpy
By the end of this project you will use the statistical capabilities of the Python Numpy package and other packages to find the statistical significance of student test data from two student groups. The T-Test is well known in the field of statistics. It is used to test a hypothesis using a set of data sampled from the population. To perform the T-Test, the population sample size, the mean, or average, of each population, and the standard deviation are all required. These will all be calculated in this project. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions...
3. Bayesian Statistics: Time Series Analysis
This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models. Time series analysis is concerned with modeling the dependency among elements of a sequence of temporally related variables. To succeed in this course, you should be familiar with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference. You will learn how to build models that can describe temporal dependencies and how to perform Bayesian inference and forecasting for the models. You will apply what you've learned with the open-source, freely available software R with sample databases. Your instructor Raquel Prado will take you from basic concepts for modeling temporally dependent data to implementation of specific classes of models...
4. Python for Statistical Analysis
Welcome to Python for Statistical Analysis! This course is designed to position you for success by diving into the real-world of statistics and data science. Learn through real-world examples: Instead of sitting through hours of theoretical content and struggling to connect it to real-world problems, we'll focus entirely upon applied statistics. Taking theory and immediately applying it through Python onto common problems to give you the knowledge and skills you need to excel. Presentation-focused outcomes: Crunching the numbers is easy, and quickly becoming the domain of computers and not people. The skills people have are interpreting and visualising outcomes and so we focus heavily on this, integrating visual output and graphical exploration in our workflows. Plus, extra bonus content on great ways to spice up visuals for reports, articles and presentations, so that you can stand out from the crowd. Modern tools and workflows: This isn't school, where we want to spend hours grinding through problems by hand for reinforcement learning. No, we'll solve our problems using state-of-the-art techniques and code libraries, utilising features from the very latest software releases to make us as productive and efficient as possible. Don't reinvent the wheel when the industry has moved to rockets...
5. Statistics/Data Analysis with SPSS: Descriptive Statistics
November, 2019. Get marketable and highly sought after skills in this course that will increase your knowledge of data analytics, with a focus on descriptive statistics, an important tool for understanding trends in data and making important business decisions. Enroll now to join the more than 2000 students and get instant access to all course content! Whether a student or professional in the field, learn the important basics of both descriptive statistics and IBM SPSS so that you can perform data analyses and start using descriptive statistics effectively. By monitoring and analyzing data correctly, you can make the best decisions to excel in your work as well as increase profits and outperform your competition. This beginner's course offers easy to understand step-by-step instructions on how to make the most of IBM SPSS for data analysis. Make Better Business Decisions with SPSS Data Analysis Create, Copy, and Apply Value LabelsInsert, Move, Modify, Sort, and Delete VariablesCreate Charts and GraphsMeasure Central Tendency, Variability, z-Scores, Normal Distribution, and Correlation Interpret and Use Data Easily and Effectively with IBM SPSS IBM SPSS is a software program designed for analyzing data. You can use it to perform every aspect of the analytical process, including planning, data collection, analysis, reporting, and deployment. This introductory course will show you how to use SPSS to run analyses, enter and code values, and interpret data correctly so you can make valid predictions about what strategies will make your organization successful. Contents and Overview This course begins with an introduction to IBM SPSS. It covers all of the basics so that even beginners will feel at ease and quickly progress. You'll tackle creating value labels, manipulating variables, modifying default options, and more. Once ready, you'll move on to learn how to create charts and graphs, such as histograms, stem and leaf plots, and more. You'll be able to clearly organize and read data that you've collected. Then you'll master central tendency, which includes finding the mean, median, and mode. You'll also learn how to measure the standard deviation and variance, as well as how to find the z-score. The course ends with introductory statistics video lectures that dive deeper into graphs, central tendency, normal distribution, variability, and z-scores. Upon completion of this course, you'll be ready to apply what you've learned to excel in your statistics classes and make smarter business decisions. You'll be able to use the many features in SPSS to gather and interpret data more effectively, as well as plan strategies that will yield the best results as well as the highest profit margins...
6. Statistics / Data Analysis in SPSS: Inferential Statistics
November, 2019. Join more than 1,000 students and get instant access to this best-selling content - enroll today! Get marketable and highly sought after skills in this course that will substantially increase your knowledge of data analytics, with a focus in the area of significance testing, an important tool for A/B testing and product assessment. Many tests covered, including three different t tests, two ANOVAs, post hoc tests, chi-square tests (great for A/B testing), correlation, and regression. Database management also covered! Two in-depth examples provided of each test for additional practice. This course is great for professionals, as it provides step by step instruction of tests with clear and accurate explanations. Get ahead of the competition and make these tests important parts of your data analytic toolkit! Students will also have the tools needed to succeed in their statistics and experimental design courses. Data Analytics is an rapidly growing area in high demand (e. g., McKinsey)Statistics play a key role in the process of making sound business decisions that will generate higher profits. Without statistics, it's difficult to determine what your target audience wants and needs. Inferential statistics, in particular, help you understand a population's needs better so that you can provide attractive products and services. This course is designed for business professionals who want to know how to analyze data. You'll learn how to use IBM SPSS to draw accurate conclusions on your research and make decisions that will benefit your customers and your bottom line. Use Tests in SPSS to Correctly Analyze Inferential Statistics Use the One Sample t Test to Draw Conclusions about a PopulationUnderstand ANOVA and the Chi SquareMaster Correlation and RegressionLearn Data Management Techniques Analyze Research Results Accurately to Make Better Business Decisions With SPSS, you can analyze data to make the right business decisions for your customer base. And by understanding how to use inferential statistics, you can draw accurate conclusions about a large group of people, based on research conducted on a sample of that population. This easy-to-follow course, which contains illustrative examples throughout, will show you how to use tests to assess if the results of your research are statistically significant. You'll be able to determine the appropriate statistical test to use for a particular data set, and you'll know how to understand, calculate, and interpret effect sizes and confidence intervals. You'll even know how to write the results of statistical analyses in APA format, one of the most popular and accepted formats for presenting the results of statistical analyses, which you can successfully adapt to other formats as needed. Contents and Overview This course begins with a brief introduction before diving right into the One Sample t Test, Independent Samples t Test, and Dependent Samples t Test. You'll use these tests to analyze differences and similarities between sample groups in a population. This will help you determine if you need to change your business plan for certain markets of consumers. Next, you'll tackle how to use ANOVA (Analysis of Variance), including Post-hoc Tests and Levene's Equal Variance Test. These tests will also help you determine what drives consumer decisions and behaviors between different groups. When ready, you'll master correlation and regression, as well as the chi-square. As with all previous sections, you'll see illustrations of how to analyze a statistical test, and you'll access additional examples for more practice. Finally, you'll learn about data management in SPSS, including sorting and adding variables. By the end of this course, you'll be substantially more confident in both IBM SPSS and statistics. You'll know how to use data to come to the right conclusions about your market. By understanding how to use inferential statistics, you'll be able to identify consumer needs and come up with products and/or services that will address those needs effectively. Join the over 1,000 students who have taken this best-selling course - enroll today!...
7. Python and Statistics for Financial Analysis
Course Overview: https://youtu.be/JgFV5qzAYno Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data. By the end of the course, you can achieve the following using python: - Import, pre-process, save and visualize financial data into pandas Dataframe - Manipulate the existing financial data by generating new variables using multiple columns - Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts - Build a trading model using multiple linear regression model - Evaluate the performance of the trading model using different investment indicators Jupyter Notebook environment is configured in the course platform for practicing python coding without installing any client applications...
8. Essential Statistics for Data Analysis
This is a hands-on, project-based course designed to help you learn and apply essential statistics concepts for data analysis & business intelligence. Our goal is to simplify and demystify the world of statistics using familiar tools like Microsoft Excel, and empower everyday people to understand and apply these tools and techniques - even if you have absolutely no background in math or stats! We'll start by discussing the role of statistics in business intelligence, the difference between sample and population data, and the importance of using statistical techniques to make smart predictions and data-driven decisions. Next we'll explore our data using descriptive statistics and probability distributions, introduce the normal distribution and empirical rule, and learn how to apply the central limit theorem to make inferences about populations of any type. From there we'll practice making estimates with confidence intervals, and using hypothesis tests to evaluate assumptions about unknown population parameters. We'll introduce the basic hypothesis testing framework, then dive into concepts like null and alternative hypotheses, t-scores, p-values, type I vs. type II errors, and more. Last but not least, we'll introduce the fundamentals of regression analysis, explore the difference between correlation and causation, and practice using basic linear regression models to make predictions using Excel's Analysis Toolpak. Throughout the course, you'll play the role of a Recruitment Analyst for Maven Business School. Your goal is to use the statistical techniques you've learned to explore student data, predict the performance of future classes, and propose changes to help improve graduate outcomes. You'll also practice applying your skills to 5 real-world BONUS PROJECTS, and use statistics to explore data from restaurants, medical centers, pharmaceutical companys, safety teams, airlines, and more. COURSE OUTLINE: Why Statistics?Discuss the role of statistics in the context of business intelligence and decision-making, and introduce the statistics workflowUnderstanding Data with Descriptive StatisticsUnderstand data using descriptive statistics, including frequency distributions and measures of central tendency & variabilityPROJECT #1: Maven Pizza ParlorModeling Data with Probability DistributionsModel data with probability distributions, and use the normal distribution to calculate probabilities and make value estimatesPROJECT #2: Maven Medical CenterThe Central Limit TheoremIntroduce the Central Limit Theorem, which leverages the normal distribution to make inferences on populations with any distributionMaking Estimates with Confidence IntervalsMake estimates with confidence intervals, which use sample statistics to define a range where an unknown population parameter likely liesPROJECT #3: Maven PharmaDrawing Conclusions with Hypothesis TestsDraw conclusions with hypothesis tests, which let you evaluate assumptions about population parameters using sample statisticsPROJECT #4: Maven Safety CouncilMaking Predictions with Regression AnalysisMake predictions with regression analysis, and estimate the values of a dependent variable via its relationship with independent variablesPROJECT #5: Maven AirlinesJoin today and get immediate, lifetime access to the following:7.5 hours of high-quality videoStatistics for Data Analysis PDF ebook (150+ pages)Downloadable Excel project files & solutionsExpert support and Q & A forum30-day Udemy satisfaction guaranteeIf you're an analyst, data scientist, business intelligence professional, or anyone looking to use statistics to make smart, data-driven decisions, this course is for you! Happy learning!-Enrique Ruiz (Lead Statistics & Excel Instructor, Maven Analytics)...
9. Statistical Data Analysis with SAS
COURSE ABSTRACTThis course aims to provide a comprehensive introduction to the SAS analytic software for Windows. Through a mixture of lectures and in-class examples, quizzes, and take-home assignments, students will gain experience using the SAS system for data manipulation, management and analysis. You will also expect A LOT of extracurricular learning materials for self-pace learning, treat it as a BONUS! Emphasis will be placed on the skills and techniques necessary for efficient data manipulation, management and analysis. It is designed for students with little to no background with SAS, and an understanding of the basic statistical concepts. This will be an excellent choice for your first SAS introduction course for your data analysis career. Plus, you will get a FREE course - SAS Data Issue Handling and Good Programming Practice (check out in the bonus lecture)!!! WHAT DO I EXPECT? A comprehensive course design from SAS basics to statistical analysisMany in-class examples, exercises and take-home assignment Master various techniques for data importingSolid understanding of variable attributes, and learn various character/numeric functions IF-THEN/ELSE statements Do loop and counter variables Master DATA step with Concatenation, Merge, etc. Exposed to several useful PROC step (PRINT, SORT, TRANSPOSE, etc.). Descriptive statistics procedures (MEANS, UNIVARIATE, FREQ)Hypothesis testing (UNIVARIATE, TTEST, ANOVA) Correlations (CORR)Regression (REG)PREREQUISITE COURSES AND KNOWLEDGE: No SAS background required; Basic knowledge of statistics is preferred...
10. Statistical Analysis Excel 2013 Essentials
Big Data is Big Business - learn the ins and outs of statistical analysis with Excel. Learn statistical analysis with Excel 2013 on your own terms with this one-of-a-kind video training course. Big data is big business, and many professionals are turning to Excels data analysis tools to help them make sense of their organizational data. Now, you can learn the ins and outs of statistical analysis with Excel - from importing data toutilizing Pivot Tables - with Statistical Analysis with Excel 2013 Essentials. Our instructors provide expert, straight-forward, user-friendly training to help you tackle new topics the way you want to learn. Master basic skills, boost your creativity, and challenge yourself in bold new directions. Understand Excels data analysis capabilities, even if you've never taken a statistics course. Work with data, normal distributions, hypothesis testing, variances, rank, and percentiles. Take advantage of formulas, Pivot Tables, dashboards, and reporting tools to display your data Includes 4 hours of statistical analysis with Excel 2013 video training. This coursem also includes lessons that allow you to practice and master the skills you have learned. If you're looking for real-world statistical analysis with Excel 2013 instruction, you'll find it here...
11. Data Analysis Excel for Beginners: Statistical Data Analysis
Teaching 11 Courses in Excel and Data Analysis! OVER 100,000 visitors visit my blog ExcelDemy dot com every month!! OVER 134,141 successful students have already taken my online courses since November 2015 with 8,019 total Reviews!!!**************************************************************************************What students are saying about this course?~ Very clear, concise explanation of basic and more advanced statistical Excel functions - Donna M Knapp~ This is an excellent well taught course. The explanations are clear and concise. The course moves at a comfortable pace. I learned a lot from this course and shouldn't have any difficulty applying the concepts to future projects. Well done. - Bill Hengen**************************************************************************************Welcome to my brand new course on Data Analysis in Excel with Statistics: Get Meanings of Data. I want to start with a quote from Daniel Egger. He is a professor at Duke University. He says: "No commercial for-profit company that is in a competitive market can remain profitable or even survive over the next five years without incorporating best practices for business data analytics into their operations."So learning how to analyze data will be the most valuable expertise in your career in the next five years. Excel will analyze and visualize data easily - this is why Excel is created and this is why Excel is the most popular spreadsheet program in the world. Microsoft Company has added new data analysis features, functions, and tools in every new version of Excel. Before going into the course: I want to warn you about something. Excel is just a tool. To analyze data you will use this tool. But analyzing data requires that you know some basic statistics and probability theories. Most of the statistics and probability concepts that are necessary to analyze data effectively are covered in your undergraduate level courses. But in this course, at first, I have discussed the theory at first, then I have advanced to teach you how to use that theory in business with the help of Excel. Let's discuss now what I will cover in this course. It is tough to build a course on data analysis using Excel as so many topics are there to be covered. So I have used the guidelines of the Project Management Institute (PMI) to create this course. The topics I am going to cover in this course are: Overview of Data analysis: I will start with an overview of the data analysis. I will describe how you will calculate common measures of your data, I will introduce you to the central limit theory and then I will provide my advice for minimizing error in your calculations. Visualizing Data: Then I will teach you how to visualize your data using histograms, how to identify relationships among data by creating XY Scatter charts and forecast future results based on Existing data. Building Hypothesis: Then I will show you how to formulate a null and alternative hypothesis, how to interpret the results of your analysis, and how to use the normal, binomial, and Poisson distributions to model your data. Relationships between data sets: Finally I will show you how to analyze relationships between data sets using co-variance, how to identify the strength of those relationships through correlation, and then I will introduce you to Bayesian analysis. Case Study: Summarizing Data by Using HistogramsCase Study: Summarizing Data by Using Descriptive StatisticsCase Study: Estimating Straight-line RelationshipsCase Study: Modeling Exponential GrowthCase Study: Using Correlations to Summarize RelationshipsCase Study: Using Moving Averages to Understand Time SeriesAnalyzing Business data is a must need expertise for every employee of a company. Your company will not survive another five years if it does not take serious business data. And you could be the best employee in your company to direct the business in the smartest way. So keep learning business data analysis with this course...
12. Statistical Analysis with R for Public Health
Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health.\n\nIn this specialisation, you’ll take a peek at what medical research is and how – and indeed why – you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions. Then you’ll get your hands dirty with analysing data sets covering some big public health challenges – fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation – using R, one of the most widely used and versatile free software packages around.\n\nThis specialisation consists of four courses – statistical thinking, linear regression, logistic regression and survival analysis – and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019.\n\nThe specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data...
13. Statistics in R - The R Language for Statistical Analysis
Do you want to learn more about statistical programming? Are you in a quantitative field? You want to know how to perform statistical tests and regressions? Do you want to hack the learning curve and stay ahead of your competition? If YES came to your mind to some of those points - read on! This tutorial will teach you anything you need to know about descriptive and inferential statistics as well as regression modeling in R. While planing this course we were focusing on the most important inferential tests that cover the most common statistical questions. After finishing this course you will understand when to use which specific test and you will also be able to perform these tests in R. Furthermore you will also get a very good understanding of regression modeling in R. You will learn about multiple linear regressions as well as logistic regressions. According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own. Should you need a more basic course on R programming we would highly recommend our R Level 1 course. The Level 1 course covers all the basic coding strategies that are essential for your day to day programming. What R you waiting for? Martin...
14. Statistical Analysis and Research using Excel
Statistical Analysis and Research using Excel is a blended learning program of theoretical knowledge with its application in Microsoft Excel software. This course is a base to all the analytical studies and research studies. It is focused on more industry relevant examples and situations, where in you learn how you actually need to apply your research and analytical skills at workplace. This can also be considered as a foundation course if you are looking out for higher education in research and analytics. From the work perspective, this course is more suitable to jobs for data engineering, data analyst and marketing research jobs...
15. Statistics for Data Analysis Using R
Perform simple or complex statistical calculations using R Programming! - You don't need to be a programmer for this:)Learn statistics, and apply these concepts in your workplace using R. The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and data sets are used to explain the application. I will explain the basic theory first, and then I will show you how to use R to perform these calculations. The following areas of statistics are covered: Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation. (Using base R function and the psych package)Data Visualization - 3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot (using base R commands)Probability - Basic Concepts, Permutations, Combinations (Basic theory only)Population and Sampling - Basic concepts (theory only)Probability Distributions - Normal, Binomial and Poisson Distributions (Base R functions and the visualize package)Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, F Test, Chi-Square TestANOVA - Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using R. What are other students saying about this course?This course is a perfect mix of theory and practice. I highly recommend it for those who want to not only get good with R, but to also become proficient in statistics. (5 stars by Aaron Verive)You get both the "how" and "why" for both the statistics and R programming. I'm really happy with this course. (5 stars by Elizabeth Crook)Sandeep has such a clear approach, pedagogic and explains everything he does. Perfect for a novice like myself. (5 stars by Hashim Al-Haboobi)Very clear explanation. Coming from a non-technical background, it is immensely helpful that Prof. Sandeep Kumar is explaining all the minor details to prevent any scope for confusion. (5 stars by Ann Mary Biju)I had a limited background in R and statistics going into this course. I feel like this gave me the perfect foundation to progress to more complex topics in both of those areas. I'm very happy I took this course. (5 stars by Thach Phan)Dr. Kumar is a fantastic teacher who takes you step by step. Can't say enough about his approach. Detailed. Not only clear descriptions of statistics but you will learn many details that make R easier to use and understand. (5 stars by James Reynolds)This is a wonderful course, I do recommend it. The best Udemy course I took. (5 stars by Joao Alberto Arantes Do Amaral)The course exceeded my expectations and i would like to thank the instructor Mr Sandeep Kumar for creating such an amazing course. The best thing about this course is the Theory incorporated that helps you understand what you are going to code in R. I have really learnt a lot. If you a looking for the best course for R then look no further because this is the best there can be. (5 stars by Kipchumba Brian)What are you waiting for?This course comes with Udemy's 30 days money-back guarantee. If you are not satisfied with the course, get your money back. I hope to see you in the course...
16. Statistics for Data Analysis Using Python
Perform simple or complex statistical calculations using Python! - You don't need to be a programmer for this:)You are not expected to have any prior knowledge of Python. I will start with the basics. Coding exercises are provided to test your learnings. The course not only explains, how to conduct statistical tests using Python but also explains in detail, how to perform these using a calculator (as if, it was the 1960s). This will help you in gaining the real intuition behind these tests. Learn statistics, and apply these concepts in your workplace using Python. The course will teach you the basic concepts related to Statistics and Data Analysis, and help you in applying these concepts. Various examples and data-sets are used to explain the application. I will explain the basic theory first, and then I will show you how to use Python to perform these calculations. The following areas of statistics are covered: Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation. Data Visualization - Commonly used plots such as Histogram, Box and Whisker Plot and Scatter Plot, using the Matplotlib. pyplot and Seaborn libraries. Probability - Basic Concepts, Permutations, Combinations Population and Sampling - Basic conceptsProbability Distributions - Normal, Binomial and Poisson Distributions Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, F Test and Chi-Square TestANOVA - Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using Python. The Goodness of Fit and the Contingency Tables...
17. Python Regression Analysis: Statistics & Machine Learning
HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE: Regression analysis is one of the central aspects of both statistical and machine learning based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in Python in a practical hands-on manner. It explores the relevant concepts in a practical manner from basic to expert level. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting & make business forecasting related decisions... All of this while exploring the wisdom of an Oxford and Cambridge educated researcher. Most statistics and machine learning courses and books only touch upon the basic aspects of regression analysis. This does not teach the students about all the different regression analysis techniques they can apply to their own data in both academic and business setting, resulting in inaccurate modelling. My course is Different; It will help you go all the way from implementing and inferring simple OLS (ordinary least square) regression models to dealing with issues of multicollinearity in regression to machine learning based regression models. LEARN FROM AN EXPERT DATA SCIENTIST: My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I also just recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have +5 years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. This course is based on my years of regression modelling experience and implementing different regression models on real life data. THIS COURSE WILL HELP YOU BECOME A REGRESSION ANALYSIS EXPERT: Here is what we'll be covering inside the course: Get started with Python and Anaconda. Install these on your system, learn to load packages and read in different types of data in PythonCarry out data cleaning PythonImplement ordinary least square (OLS) regression in Python and learn how to interpret the results. Evaluate regression model accuracyImplement generalized linear models (GLMs) such as logistic regression using PythonUse machine learning based regression techniques for predictive modelling Work with tree-based machine learning modelsImplement machine learning methods such as random forest regression and gradient boosting machine regression for improved regression prediction accuracy.& Carry out model selectionTHIS IS A PRACTICAL GUIDE TO REGRESSION ANALYSIS WITH REAL LIFE DATA: This course is your one shot way of acquiring the knowledge of statistical and machine learning analysis that I acquired from the rigorous training received at two of the best universities in the world, perusal of numerous books and publishing statistically rich papers in renowned international journal like PLOS One. Specifically the course will: (a) Take you from a basic level of statistical knowledge to performing some of the most common advanced regression analysis based techniques. (b) Equip you to use Python for performing the different statistical and machine learning data analysis tasks. (c) Introduce some of the most important statistical and machine learning concepts to you in a practical manner so you can apply these concepts for practical data analysis and interpretation. (d) You will get a strong background in some of the most important statistical and machine learning concepts for regression analysis. (e) You will be able to decide which regression analysis techniques are best suited to answer your research questions and applicable to your data and interpret the results. It is a practical, hands-on course, i. e. we will spend some time dealing with some of the theoretical concepts related to both statistical and machine learning regression analysis. However, majority of the course will focus on implementing different techniques on real data and interpret the results. After each video you will learn a new concept or technique which you may apply to your own projects. JOIN THE COURSE NOW!...
18. Introduction to Statistics & Data Analysis in Public Health
Welcome to Introduction to Statistics & Data Analysis in Public Health! This course will teach you the core building blocks of statistical analysis - types of variables, common distributions, hypothesis testing - but, more than that, it will enable you to take a data set you've never seen before, describe its keys features, get to know its strengths and quirks, run some vital basic analyses and then formulate and test hypotheses based on means and proportions. You'll then have a solid grounding to move on to more sophisticated analysis and take the other courses in the series. You'll learn the popular, flexible and completely free software R, used by statistics and machine learning practitioners everywhere. It's hands-on, so you'll first learn about how to phrase a testable hypothesis via examples of medical research as reported by the media. Then you'll work through a data set on fruit and vegetable eating habits: data that are realistically messy, because that's what public health data sets are like in reality. There will be mini-quizzes with feedback along the way to check your understanding. The course will sharpen your ability to think critically and not take things for granted: in this age of uncontrolled algorithms and fake news, these skills are more important than ever. Prerequisites Some formulae are given to aid understanding, but this is not one of those courses where you need a mathematics degree to follow it. You will need only basic numeracy (for example, we will not use calculus) and familiarity with graphical and tabular ways of presenting results. No knowledge of R or programming is assumed...
19. Data Analysis & Statistics: practical course for beginners
Find out why data planning is like a bank robbery and why you should explore data like Indiana Jones. Get to know the poisonous triangle of data collection and see how data can be spoiled during preparation with one bad ingredient. Learn why data analysis itself is the cherry on top and understand why data analysis is all about the money and what to do about it. If you ever wanted to learn data analysis and statistics, but thought it was too complicated or time consuming, you're in the right place. Start using powerful scientific methods in a simple way. This is the data analysis and statistics course you've been waiting for. Practical, easy to understand, straight to the point. This course will give you the complete package to be very effective in analyzing data and using statistics. Throughout the course we will use the mobile shopping case study, which makes learning fun along the way. Main features of this course: Provides you the complete package to be comfortable using statistics and analyzing dataCovers all stages of data analysis processVery easy to understandNo complicated equationsPlain English instead of multiple statistical termsPractical, with mobile shopping case studyExercises and quizzes to help you master data analysis and statisticsReal world dataset and other materials to downloadMore than 70 high quality videosWhy should you take this course?Data analysis is becoming more and more popular and important every year. You don't have to become data science guru or master of data mining overnight, but you should know how to analyze and use data in practice. You should be able to effectively work with real world, business data on your own. And this course is all about giving you just that in the quickest and easiest way possible. You won't waste time for theoretical concepts relevant to geeks and teachers only. We will dive directly into the key knowledge and methods. You will follow the intuitive step-by-step process, with examples, quizzes and exercises. The same process that is utilized by the most successful companies. At the end of the course you will feel comfortable with data analysis tasks and use of the most important statistics. This course is a first step you need to take into the world of professional data analysis and you don't need any experience to take it. Go beyond Excel analysis and surprise your boss with valuable insight. Or learn for the benefit of your own company. Whatever is your motivation to start with data analysis and statistics, you're in the right place. This complete course is divided into six essential chapters that corresponds with the six parts of data analysis process - data planning, data exploration, data collection, data preparation, data analysis and data monetization. All of this explained in a pleasant and accessible way, just like your colleague would explain this to you. And obviously you have 30 days money back guarantee, if you don't like this course for any reason. But I do everything in my power for you not only to like the course, but to love it. A lot of people will tell you that you have to learn programming languages to analyze data effectively, but it's not true and you will see it in this course. Programming background is nice, but you don't have to know any programming language to uncover the power of data. Understanding data analysis and statistics is not far away. It is the key competence on the job market, but also in everyday life. Remember that no great decision has ever been made without it. Sign up for this course today and immediately improve the skills essential for your success...
20. Statistical Analysis with Excel 2013 Advanced Skills
Big data is big business - Excels data analysis tools to help them make sense of their organizational data. Master statistical analysis with Excel 2013 on your own terms with this one-of-a-kind video training course. Big data is big business, and many professionals are turning to Excels data analysis tools to help them make sense of their organizational data. Now, you can learn the ins and outs of statistical analysis with Excel - from importing data to utilizing Pivot Tables - with Statistical Analysis with Excel 2013 Advanced Skills. Our instructors provide expert, straight-forward, user-friendly training to help you tackle new topics the way you want to learn. Master basic skills, boost your creativity, and challenge yourself in bold new directions. Understand Excels data analysis capabilities and how they can be applied in your organization. Work with data, normal distributions, hypothesis testing, variances, rank, and percentiles. Take advantage of formulas, Pivot Tables, dashboards, and reporting tools to display your data. Includes 5 hours of advanced statistical analysis with Excel 2013 video training with lessons for you to practice what you have learned. If you're looking for advanced real-world statistical analysis with Excel 2013 instruction, you'll find it here...