The top 20 Statistical Data courses you need to take

1. Advanced Statistics for Data Science

Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.\n\nThis specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications. These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling.\n\nThis specialization also linear models for data science, starting from understanding least squares from a linear algebraic and mathematical perspective, to statistical linear models, including multivariate regression using the R programming language. These courses will give learners a firm foundation in the linear algebraic treatment of regression modeling, which will greatly augment applied data scientists' general understanding of regression models.\n\nThis specialization requires a fair amount of mathematical sophistication. Basic calculus and linear algebra are required to engage in the content...

2. Data Science Foundations: Statistical Inference

This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language.\n\nThis specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.\n\nLogo adapted from photo by Christopher Burns on Unsplash...

3. Statistical Learning for Data Science

Statistical Learning is a crucial specialization for those pursuing a career in data science or seeking to enhance their expertise in the field. This program builds upon your foundational knowledge of statistics and equips you with advanced techniques for model selection, including regression, classification, trees, SVM, unsupervised learning, splines, and resampling methods. Additionally, you will gain an in-depth understanding of coefficient estimation and interpretation, which will be valuable in explaining and justifying your models to clients and companies. Through this specialization, you will acquire conceptual knowledge and communication skills to effectively convey the rationale behind your model choices and coefficient interpretations.\n\nThis specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder...

4. Statistics for Data science

4

(100)

When you talk about data science the most important thing is  Statistical MATHS. This course teaches statistical maths using simple excel. My firm belief is MATHS is 80% part of data science while programming is 20%. If you start data science directly with python , R and so on , you would be dealing with lot of technology things but not the statistical things. I recommend start with statistics first using simple excel and the later apply the same using python and R. Below are the topics covered in this course. Lesson 1:- What is Data science ?Chapter 1:- What is Data science  and why do we need it ?Chapter 2:- Average , Mode , Min and Max using simple Excel. Chapter 3:- Data science is Multi-disciplinary. Chapter 4:- Two golden rules for maths for data science. Lesson 2:- What is Data science ?Chapter 4:- Spread and seeing the same visually. Chapter 5:- Mean, Median, Mode, Max and MinChapter 6:- Outlier, Quartile & Inter-QuartileChapter 7:- Range and SpreadLesson 3 - Standard Deviation, Normal Distribution & Emprical Rule. Chapter 8:- Issues with Range spread calculationChapter 9:- Standard deviationChapter 10:- Normal distribution and bell curve understandingChapter 11:- Examples of Normal distributionChapter 12:- Plotting bell curve using excelChapter 13:- 1 , 2 and 3 standard deviationChapter 14:- 68,95 and 98 emprical rule. Chapter 15:- Understanding distribution of 68,95 and 98 in-depth. Lesson 4:- The ZScore calculationChapter 16:- Probability of getting 50% above and 50% less. Chapter 17:- Probability of getting 20 value. Chapter 18:- Probability of getting 40 to 60. Lesson 5 - Binomial distributionChapter 22:- Basics of binomial distribution. Chapter 23:- Calculating existing probability from history. Chapter 24:- Exact vs Range probability. Chapter 25:- Applying binomial distribution in excel. Chapter 26:- Applying Range probability. Chapter 27:- Rules of Binomial distribution...

5. Statistics/Data Analysis with SPSS: Descriptive Statistics

4.5

(1,113)

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

4.7

(1,622)

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. Data Science: Statistics and Machine Learning

Build models, make inferences, and deliver interactive data products.\n\nThis specialization continues and develops on the material from the Data Science: Foundations using R specialization. It covers statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, learners will have a portfolio demonstrating their mastery of the material.\n\nThe five courses in this specialization are the very same courses that make up the second half of the Data Science Specialization. This specialization is presented for learners who have already mastered the fundamentals and want to skip right to the more advanced courses...

8. Statistical Modeling for Data Science Applications

Statistical modeling lies at the heart of data science. Well crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In this three credit sequence, learners will add some intermediate and advanced statistical modeling techniques to their data science toolkit. In particular, learners will become proficient in the theory and application of linear regression analysis; ANOVA and experimental design; and generalized linear and additive models. Emphasis will be placed on analyzing real data using the R programming language.\n\nThis specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.\n\nLogo adapted from photo by Vincent Ledvina on Unsplash...

9. Statistics & Mathematics for Data Science & Data Analytics

4.6

(1,486)

Are you aiming for a career in Data Science or Data Analytics?Good news, you don't need a Maths degree - this course is equipping you with the practical knowledge needed to master the necessary statistics. It is very important if you want to become a Data Scientist or a Data Analyst to have a good knowledge in statistics & probability theory. Sure, there is more to Data Science than only statistics. But still it plays an essential role to know these fundamentals ins statistics. I know it is very hard to gain a strong foothold in these concepts just by yourself. Therefore I have created this course. Why should you take this course?This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with dataThis course is taught by an actual mathematician that is in the same time also working as a data scientist. This course is balancing both: theory & practical real-life example. After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis. What is in this course?This course is giving you the chance to systematically master the core concepts in statistics & probability, descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions , decision trees and more. In real-life examples you will learn the stats knowledge needed in a data scientist's or data analyst's career very quickly. If you feel like this sounds good to you, then take this chance to improve your skills und advance career by enrolling in this course...

10. Essential Statistics for Data Analysis

4.7

(606)

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)...

11. Statistical Data Analysis with SAS

4.4

(575)

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...

12. Excel Statistics / Data Analytics

4.7

(158)

Get marketable data analytic skills in this course using Microsoft Excel. This course will substantially increase your knowledge of data analytics using the extremely popular Microsoft Excel software program, with a focus in the area of significance testing, an important tool for A/B testing and product assessment. Many tests covered, including different t tests, ANOVA, post hoc tests, correlation, and regression.  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.  In this course, a number statistical procedures will be covered in Microsoft Excel using the Data Analysis TookPak. The following statistical procedures will be covered in this course: Central Tendency (Mean, Median, Mode)Variability (Standard Deviation, Variance, Range)t tests (Independent t and Dependent t)Analysis of Variance (ANOVA)Post Hoc TestsCorrelationMultiple Regression  For each test, we will cover: How to perform the analysis step-by-step in Microsoft ExcelWhat the results mean in clear, yet accurate languageHow to effectively, accurately, and professionally communicate the results to others, clearly demonstrating your understanding of the material. Enroll in this course and obtain important marketable data analytic skills today!...

13. Statistics & Probability for Data Science

4.2

(108)

Building on the Foundation: Probability, Descriptive Statistics- Part2, Data Visualization, Covariance and CorrelationIn this course we build your foundation on Data Science. Now that you have the insights and clarity on what data science is about after going through our Part 1 Course, we will take you through sessions on Probability, Descriptive Statistics, Data Visualization, Histogram, Boxplot & Scatter plot, You will learn that Covariance gives an idea about the direction of the data with examples, you will also learn Correlation with examples. Probability is the mathematical term for the likelihood that something will occur, such as drawing an ace from a deck of cards or picking a green piece of candy from a bag of assorted colors. You use probability in daily life to make decisions when you don't know for sure what the outcome will be. Most of the time, you won't perform actual probability problems, but you'll use subjective probability to make judgment calls and determine the best course of action. Descriptive statistics consist of methods for organizing and summarizing information (Weiss, 1999). Descriptive statistics include the construction of graphs, charts, and tables, and the calculation of various descriptive measures such as averages, measures of variation, and percentiles. Data visualization is a graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions. Covariance and Correlation are two concepts in the field of probability and statistics. Both are concepts that describe the relationship between two variables to each other. Also, both are tools of measurement of a certain kind of dependence between variables. Through our Four-part series we will take you step by step, this is our second part which will build your foundation. Testimonials: I'm glad I joined this class Couldn't have wished for any other ~ Reginald Owusu Ansahit was too good ~ Harsha VardhanI appreciate that the faculty explains the practical life uses with the theory, so it's not just a bunch of dry math. I need to understand the why behind the how or else I fail to engage. For the most part I was able to follow the examples and they were clear. I got so engrossed in the topic I lost track of time, so maybe I'm enjoying this. ~ Sheri FYes, It's very helpful to increase my knowledge on Data science. Very interesting class. ~ Ch HemalathaIts educative, improving my skill and knowledge. ~ Atimiwoaye Adetunji Dayo...

14. Introduction to Computational Statistics for Data Scientists

The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST). What it does cover is:\n\nThe basics of Bayesian statistics and probability\n\nUnderstanding Bayesian inference and how it works\n\nThe bare-minimum set of tools and a body of knowledge required to perform Bayesian inference in Python, i.e. the PyData stack of NumPy, Pandas, Scipy, Matplotlib, Seaborn and Plot.ly\n\nA scalable Python-based framework for performing Bayesian inference, i.e. PyMC3\n\nWith this goal in mind, the content is divided into the following three main sections (courses).\n\nIntroduction to Bayesian Statistics - The attendees will start off by learning the the basics of probability, Bayesian modeling and inference in Course 1.\n\nIntroduction to Monte Carlo Methods - This will be followed by a series of lectures on how to perform inference approximately when exact calculations are not viable in Course 2.\n\nPyMC3 for Bayesian Modeling and Inference - PyMC3 will be introduced along with its application to some real world scenarios.\n\nThe lectures will be delivered through Jupyter notebooks and the attendees are expected to interact with the notebooks...

15. Statistical Inference for Estimation in Data Science

This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash...

16. Data and Statistics Foundation for Investment Professionals

Aimed at investment professionals or those with investment industry knowledge, this course offers an introduction to the basic data and statistical techniques that underpin data analysis and lays an essential foundation in the techniques that are used in big data and machine learning. It introduces the topics and gives practical examples of how they are used by investment professionals, including the importance of presenting the “data story" by using appropriate visualizations and report writing. In this course you will learn how to: - Explain basic statistical measures and their application to real-life data sets - Calculate and interpret measures of dispersion and explain deviations from a normal distribution - Understand the use and appropriateness of different distributions - Compare and contrast ways of visualizing data and create them using Python (no prior knowledge of Python necessary) - Explain sampling theory and draw inferences about population parameters from sample statistics - Formulate hypotheses on investment problems This course is part of the Data Science for Investment Professionals Specialization offered by CFA Institute...

17. Statistics & Data Analytics For Data Science And Business

4.2

(190)

*Highest Rated Course In This Category*Excellent course! Dr. Bateh does a great job taking a deep dive into statistics and data analytics, and how they can be applied to every day business. I highly recommend this course! - Robert M.*Excellent course! Dr. Bateh does a great job taking a deep dive into statistics and data analytics, and how they can be applied to every day business. I highly recommend this course! - Tyler B.*I highly recommend this course! Dr. Bateh does a wonderful job explaining complex statistical concepts in an easy to understand and practical manner. I will definitely be applying my learnings in the workplace. - Faris K. WHY LEARN STATISTICS FOR BUSINESS DECISION-MAKING?More and more organizations around the globe are expecting professionals will make data-driven decisions. Employees, team leaders, managers, and executives who can think quantitatively are in high demand. This course is for beginners in statistics and covers the role statistics plays in the business world and in making data-driven decisions. WHY LEARN STATISTICS FOR DATA SCIENCE CAREERS?Data Science enables companies to efficiently understand gigantic data from multiple sources and derive valuable insights to make smarter data-driven decisions. Data Science is widely used in various industry domains, including marketing, healthcare, finance, banking, policy work, and more. in data science, statistics is at the core of sophisticated machine learning algorithms, capturing and translating data patterns into actionable evidence. Data scientists use statistics to gather, review, analyze, and draw conclusions from data, as well as apply quantified mathematical models to appropriate variables. WHY THIS COURSE?The goal of this course is to improve your ability to identify a problem, collect data, and organize and analyze data, which will aid in making more effective decisions. You will learn techniques that can help any manager or businessperson responsible for decision-making. This course has been created for decision-makers whose primary goal is not just to do the calculation and the analysis, but to understand and interpret the results and make recommendations to key stakeholders. You will also learn how the popular business software, Microsoft Excel, can support this process. This course will provide you with a solid foundation for thinking quantitatively within your company. HOW IS THIS A HIGHLY PRACTICAL COURSE?To help facilitate this objective, this course follows two fictitious companies that encounter a series of business problems, while demonstrating how managers would use the concepts in the book to solve these problems and determine the next course of action. WHO IS THIS COURSE FOR?This course is intended for beginners, and the student does not require prior statistical training. All computations will be completed using Microsoft Excel and training will be provided within the course. WHAT AM I GOING TO LEARN?Professor Bateh's Introductions - each module includes a clear and concise introduction by Dr. Justin Bateh on what you will learn in each module, and most importantly, why it's important. Theoretical Explanation - Dr. Bateh's team of associates provide a deep into the theory of statistics, if you wish, to learn the how behind what we are studying. Real-World Company Problems - this course follows two fictitious companies that encounter a series of business problems while demonstrating how managers would use the concepts in the course to solve these problems and determine the next course of action. Guided Demonstrations For Solving Problems - each module has a demonstration of how to solve the real-world problem that one of the two companies we are following encountered prepared by Dr. Bateh's and his team of associates. Additional Resources - you also get Dr. Bateh 300+ page ebook on using statistics for better business decisions + 11 customizable and automated excel templates.----------FEEDBACK--------------I have taken courses with Professor Bateh and they were my favorite classes. The classes were both interesting, and the information taught was insightful. He did a wonderful job at answering the many questions I had and providing quick, thorough feedback. I highly recommend taking his classes.  - Pamela ScottDr. Bateh's presentations are excellent. His enthusiasm about the topics he tackles makes the class more interesting. Using analogies and real-life examples, he simplifies and makes accessible for everyone in class complex issues in business. I have 20 years of Senior Leadership experience, and Dr. Bateh, even when he is humble, has solid knowledge and experience in the field. He shares that with anyone open to learning. I consider him as a reference for me on those topics and in all of my career, I had been in contact with many academics, but few as valuable for learning as Justin Bateh.  - Dr. Gustavo OliveraI had the privilege of having Dr. Bateh as my instructor in the course. I was immediately struck by his teaching style and ability to connect with the audience. Dr. Bateh demonstrates empathy, kindness, and a deep understanding of the topics he teaches. I loved the way that he demonstrated various applications of the field. His ability to take what could be a mundane topic and make it so relevant and interesting is such a gift. Dr. Bateh is generous with his time and in fostering mentoring relationships with students and professionals alike. I would highly recommend that any student pursue the opportunity to connect with and learn from him. - Stacey PecenkaVery few people reach Justin's level of brilliance and professionalism. He is absolutely fantastic to work with, has a great sense of humor, and provided the critical insight and input necessary to make our project come together beautifully. His attention to detail and application of instruction and knowledge is outstanding, second to none, and he communicates smoothly and effortlessly. You couldn't ask for a better instructor. I highly recommend Justin and look forward to working with him again in the future. - Shannon Cooper...

18. Statistics Masterclass for Data Science and Data Analytics

4.7

(234)

Starting a career in Data Science or Business Analysis ?then this course will help you to Built a Strong Foundation of statistics for Data Science and Business Analytics This course is Very Practical, Easy to Understand and Every Concept is Explained with an Example! I have added real life examples to understand the applications of statistics in the field of Data Science... We'll cover everything that you need to know about statistics and probability for Data Science and Business Analytics! Including:1) Levels of Measurement2) Measures of Central Tendency3) Population and Sample4) Population Standard Variance5) Quartiles and IQR6) Permutations, Combinations7) Intersection, Union and Complement8) Independent and Dependent Events9) Conditional Probability10) Bayes' Theorem11) Uniform Distribution, Binomial Distribution12) Poisson Distribution, Normal Distribution, Skewness13) Standardization and Z Score14) Central Limit Theorem15) Hypothesis Testing, Type I and Type II Error16) Students T-Distribution17) ANOVA - Analysis of Variance18) F Distribution19) Linear Regression and much more... So What Are You Waiting For ?Enroll Now and Empower Your Career!...

19. Advanced Statistics and Data Mining for Data Science

4.2

(53)

Data Science is an ever-evolving field. Data Science includes techniques and theories extracted from statistics, computer science, and machine learning. This video course will be your companion and ensure that you master various data mining and statistical techniques. The course starts by comparing and contrasting statistics and data mining and then provides an overview of the various types of projects data scientists usually encounter. You will then learn predictive/classification modeling, which is the most common type of data analysis project. As you move forward on this journey, you will be introduced to the three methods (statistical, decision tree, and machine learning) with which you can perform predictive modeling. Finally, you will explore segmentation modeling to learn the art of cluster analysis. Towards the end of the course, you will work with association modeling, which will allow you to perform market basket analysis. This course uses SPSS v25, while not the latest version available, it provides relevant and informative content for legacy users of SPSS. About the Author: Jesus Salcedo has a Ph. D. in Psychometrics from Fordham University. He is an independent statistical and data mining consultant and has been using SPSS products for over 20 years. He is a former SPSS Curriculum Team Lead and Senior Education Specialist who has written numerous SPSS training courses and trained thousands of users...