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August 24, 2022
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Data analytics is the science of analyzing raw data to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.
You can think of data analytics as a form of business intelligence, used to solve specific problems and challenges within an organization. It’s all about finding patterns in a dataset which can tell you something useful and relevant about a particular area of the business—how certain customer groups behave, for example, or how employees engage with a particular tool. Data analytics helps you to make sense of the past and to predict future trends and behaviors; rather than basing your decisions and strategies on guesswork, you’re making informed choices based on what the data is telling you. Armed with the insights drawn from the data, businesses and organizations are able to develop a much deeper understanding of their audience, their industry, and their company as a whole—and, as a result, are much better equipped to make decisions and plan ahead.
 Data analytics is the science of analyzing raw data to make conclusions about that information.
 Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategicallyguided decisions.
 The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.
 Various approaches to data analytics include looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics).
 Data analytics relies on a variety of software tools ranging from spreadsheets, data visualization, and reporting tools, data mining programs, or opensource languages for the greatest data manipulation.

 Theory Unlimited
 Statistics – Big Picture Unlimited
 Basic Statistical Terminology Unlimited
 Parameter and Statistic Unlimited
 Variables and Data Unlimited
 Levels of Measurement Scales Unlimited
 Descriptive Statistics Unlimited
 Different types of Descriptive Statistics Unlimited
 AIM Unlimited
 Introduction to Python and Python versions Unlimited
 Anaconda Python Distribution Unlimited
 What are Jupyter notebooks? Unlimited
 Importing the necessary packages Unlimited
 Calculating Central Tendencies Unlimited
 Calculating Measure of Dispersion Unlimited
 Case Study Unlimited
 Descriptive Statistics Unlimited
 SUMMARY Unlimited
 Programming Assignment 1 week, 3 days
 Module1.INTRODUCTION TO STATISTICS QUIZ 00:05:00

 Theory Unlimited
 Assigning Probabilities Unlimited
 Probability – Rules Unlimited
 Probability – Types Unlimited
 Probability Distributions Unlimited
 Types of Discrete Probability Distributions Unlimited
 AIM Unlimited
 Importing the necessary packages Unlimited
 Calculating probability using binomial distribution Unlimited
 Calculating probability using Poisson distribution Unlimited
 Case Study Unlimited
 SUMMARY Unlimited
 Programming Assignment 1 week, 3 days
 Module 2. PROBABILITY DISTRIBUTIONS QUIZ 00:05:00

 Theory Unlimited
 Continuous Random Variable Unlimited
 Types of Continuous Random Variables Unlimited
 Empirical Formula Unlimited
 Uniform Distribution / Rectangular Distribution Unlimited
 Probability density function Unlimited
 Continuity Correction Factor Unlimited
 Sampling Distribution Unlimited
 Expectation and Standard deviation of 𝑋 bar Unlimited
 Two Main areas of inferential statistics Unlimited
 Proportion Unlimited
 tDistribution Unlimited
 Confidence Intervals Formulas Unlimited
 Critical Region up Close Unlimited
 Errors Unlimited
 Types of t – Test’s Unlimited
 Confidence Intervals Unlimited
 Importing the necessary packages Unlimited
 Calculating probabilities using normal and standard normal distributions Unlimited
 Calculating confidence intervals Unlimited
 Calculating confidence interval for variance Unlimited
 Performing Test of Hypothesis Unlimited
 Case Study Unlimited
 SUMMARY Unlimited
 Module 3. INFERENTIAL STATISTICS QUIZ 00:05:00
 Programming Assignment 1 week, 3 days

 Theory Unlimited
 Assumptions for ANOVA Unlimited
 Why ANOVA? Unlimited
 Comparing means Unlimited
 Comparing with Population mean Unlimited
 Why can’t we compute ttest three times? Unlimited
 What changed? Unlimited
 ANOVA: Analysis of Variance is variability ratio Unlimited
 AIM Unlimited
 Case Study Unlimited
 Task 3: Chisquare test of independence Unlimited
 SUMMARY Unlimited
 Module 4. ANOVA Analysis of Variance QUIZ 00:05:00
 Programming Assignment 1 week, 3 days

 Theory Unlimited
 Case Study Unlimited
 Imputing Missing Values Unlimited
 Univariate analysis (Single variable plots) Unlimited
 Removing Outliers Unlimited
 Bivariate Analysis Unlimited
 Correlations between Features and Target Unlimited
 Multivariate Analysis Unlimited
 SUMMARY Unlimited
 Module 5. Data Cleaning and Exploratory Data Analysis QUIZ 00:05:00
 Programming Assignment 1 week, 3 days

 Theory Unlimited
 The use of Regression Unlimited
 Linear Regression Unlimited
 Problem Statement Unlimited
 Ordinary Least Squared Estimation Unlimited
 The Mathematics involved Unlimited
 Derivation of OLS by Minimizing Errors Unlimited
 Interpreting the model Unlimited
 Prediction using the model Unlimited
 Model Confidence Unlimited
 Hypothesis Testing and pvalues Unlimited
 Assumptions of OLS Regression Unlimited
 What if these assumptions get violated? Unlimited
 Interpretation of Regression Plots Unlimited
 Model Evaluation Metrics Unlimited
 Practical Implementation Unlimited
 Describe the Data Set Unlimited
 Filtering the Dataset Unlimited
 Build the model Unlimited
 Exploring the output Unlimited
 Evaluating the model Unlimited
 Checking for HeteroScedasticity Unlimited
 Checking for Autocorrelation Unlimited
 Checking for Normally Distributed Residuals Unlimited
 Measures of Error Unlimited
 RSquared Unlimited
 Confidence Intervals Unlimited
 Hypothesis Testing Unlimited
 Create a Summary of the Model Output Unlimited
 Remove the Insignificant Variables Unlimited
 Save the Model for Future Use Unlimited
 Summary Unlimited
 Module 6. Regression Analysis – Part I QUIZ 00:05:00
 Programming Assignment 1 week, 3 days

 Theory Unlimited
 Cost function Optimization Algorithm Unlimited
 Dummy variables versus quantitative explanatory variable Unlimited
 Underfitting and Overfitting Unlimited
 Bias and Variance Tradeoff Unlimited
 Regularization Unlimited
 Polynomial Regression Unlimited
 AIM Unlimited
 Summary Unlimited
 Module 7. Regression Analysis – Part II QUIZ 00:05:00
 Programming Assignment 1 week, 3 days

 Theory Unlimited
 What is Time Series Analysis? Unlimited
 Importance of Time Series Analysis Unlimited
 Components of Time Series Unlimited
 Difference between a time series and regression problem Unlimited
 Problem Statement Unlimited
 Table of Contents Unlimited
 Understanding Data Unlimited
 Forecasting using Multiple Modeling Techniques Unlimited
 Removing Trend Unlimited
 Forecasting the time series using ARIMA Unlimited
 Summary Unlimited
 Module 8. Time Series Analysis QUIZ 00:05:00
 Programming Assignment 1 week, 3 days