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Why Study Statistics for Data Science?

Statistics is the cornerstone of Data Science. Only when you know the various statistical techniques used in analysis, would you be able to use them. Statistics provides a foundation for analyzing the performance of a research method and that’s not limited to use in just science, but it has been of widely applied in other industries like Finance, Logistics and Marketing.

This subject is a fundamental ingredient in the skillset of a Data Scientist in the modern day. It is only the specific functions of Statistics for Data Science that you need to master and our free statitics course gives you just that. 

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Highlights of Statistics For Data Science Foundations Course



  • Video based Self-Paced Course, lets you learn at your own pace 
  • Know all the vital concepts that you’ll use while working with data
  • This course will cover a range of topics ranging from descriptive statistics, probability, distributions, estimation, hypothesis testing, inference to regression
  • You can use the techniques learnt in this course irrespective of the tool you plan to use for Data Science & Analysis




  • The Admission process is simple, just enroll here and we will do the needful
  • There are no prerequisites for taking this course. Anyone with a basic understanding of mathematics is free to register
  • There are no timelines, you can start anytime  

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Statistics for Data Science Course Syllabus

Data and Statistics

– Elements, Variables, and Observations
– Scales of Measurement
– Categorical and Quantitative Data
– Cross-Sectional and Time Series Data
– Descriptive Statistics
– Statistical Inference
– Descriptive Statistics: Tabular and Graphical
– Summarizing Categorical Data
– Summarizing Quantitative Data
– Cross Tabulations and Scatter Diagram

Descriptive Statistics: Numerical Measures
– Measures of Location
– Measures of Variability
– Measures of Distribution Shape, Relative Location, and Detecting Outliers
– Box Plot
– Measures of Association Between Two Variables

Introduction to Probability
– Experiments, Counting Rules, and Assigning Probabilities
– Events and Their Probabilities
– Complement of an Event
– Addition Law
– Independent Events
– Multiplication Law
– Baye’s theorem

Discrete Probability Distributions

– Random Variables
– Discrete Probability Distributions
– Expected Value and Variance
– Binomial Probability Distribution
– Poisson Probability Distribution

Continuous Probability Distributions

– Uniform Probability Distribution
– Normal Curve
– Standard Normal Probability Distribution
– Computing Probabilities for Any Normal Probability Distribution

Sampling and Sampling Distributions

– Sampling from a Finite Population
– Sampling from an Infinite Population
– Other Sampling Methods
– Stratified Random Sampling
– Cluster Sampling
– Systematic Sampling
– Convenience Sampling
– Judgment Sampling

Interval Estimation

– Population Mean: Known
– Population Mean: Unknown
– Determining the Sample Size
– Population Proportion

Hypothesis Tests

– Developing Null and Alternative Hypotheses
– Type I and Type II Errors
– Population Mean: Known
– Population Mean: Unknown

Inference About Means and Proportions with Two Populations

– Inferences About the Difference Between Two Population Means
– Inferences About the Difference Between Two Population Means
– Inferences About the Difference Between Two Population Means
– Inferences About the Difference Between Two Population

Inferences About Population Variances

– Inferences About a Population Variance
– Inferences About Two Population Variances

Tests of Goodness of Fit and Independence

– Goodness of Fit Test: A Multinomial Population
– Test of Independence

Simple Linear Regression

– Simple Linear Regression Model
– Regression Model and Regression Equation
– Estimated Regression Equation
– Least Squares Method
– Coefficient of Determination
– Correlation Coefficient
– Model Assumptions
– Testing for Significance
– Using the Estimated Regression Equation for Estimation and Prediction
– Residual Analysis: Validating Model Assumptions
– Residual Analysis: Outliers and Influential Observations

Multiple Regression

– Multiple Regression Model
– Least Squares Method
– Multiple Coefficient of Determination
– Model Assumptions
– Testing for Significance
– Categorical Independent Variables
– Residual Analysis

Time Series Analysis and Forecasting

– Time Series Patterns
– Forecast Accuracy
– Moving Averages and Exponential Smoothing
– Trend Projection
– Seasonality and Trend
– Time Series Decomposition

Nonparametric Methods

– Sign Test
– Wilcoxon Signed-Rank Test
– Mann-Whitney-Wilcoxon Test
– Kruskal-Wallis Test
– Rank Correlation
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