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August 24, 2022

Data analytics is the process of examining data sets in order to find trends and draw conclusions about the information they contain. Increasingly, data analytics is done with the aid of specialized systems and software.
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Annapurna Singh
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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 strategically-guided 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 open-source 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
    • t-Distribution 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 t-test three times? Unlimited
    • What changed? Unlimited
    • ANOVA: Analysis of Variance is variability ratio Unlimited
    • AIM Unlimited
    • Case Study Unlimited
    • Task 3: Chi-square 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 p-values 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
    • R-Squared 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

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