INTRODUCTION TO DATA SCIENCE 4

• Lecture1.1
  What is data Science? – Introduction 30 min
• Lecture1.2
  Importance of Data Science 30 min
• Lecture1.3
  Demand for Data Science Professional 30 min
• Lecture1.4
  Lifecycle of data science 30 min
• Lecture1.5
  Tools and Technologies used in data Science 30 min
• Lecture1.6
  Business Intelligence vs Data Science vs Data Engineer 30 min
• Lecture1.7
  Role of a data scientist 30 min

Fundamentals of Math and Probability 6

INTRODUCTION TO STATISTICS

• Lecture2.1
  Basic understanding of linear algebra, Matrices, vectors 30 min
• Lecture2.2
  Addition and Multiplication of matrices 30 min
• Lecture2.3
  Fundamentals of Probability 30 min
• Lecture2.4
  Probability distributed function and cumulative distributed function 30 min
• Lecture2.5
  Conditional Probability 30 min
• Lecture2.6
  Class Hand-on – Problem solving using R for vector manipulation Problem solving for probability assignments Copy Copy 30 min

Descriptive Statistics 4

• Lecture3.1
  Describe or summaries a set of data Measure of central tendency and measure of dispersion. 30 min
• Lecture3.2
  The mean, median, mode, Standard deviation, Variance, Range, kurtosis and skewness. 30 min
• Lecture3.3
  Histograms, Bar chart, Box plot 30 min
• Lecture3.4
  Class Hands-on- 5 Point summary Box Plot, Histogram and Bar Chart Exploratory analytics R Methods Copy Copy 30 min

Inferential Statistics 17

• Lecture4.1
  What is inferential statistics Different types of Sampling techniques Central Limit Theorem 30 min
• Lecture4.2
  Univariate & Bivariate Analysis 30 min
• Lecture4.3
  Correlations 30 min
• Lecture4.4
  Least Square Regression 30 min
• Lecture4.5
  Normal Distribution 30 min
• Lecture4.6
  Binomial Distribution & Quincunx 30 min
• Lecture4.7
  Point estimate and Interval estimate 30 min
• Lecture4.8
  Creating confidence interval for population parameter Characteristics of Z- distribution and T-Distribution Basics of Hypothesis Testing Copy Copy 30 min
• Lecture4.9
  Bias & Variance trade-offs 30 min
• Lecture4.10
  Type of test and rejection region 30 min
• Lecture4.11
  Type of errors in Hypothesis resting, Type-l error and Type-ll errors 30 min
• Lecture4.12
  False Positive & False Negative 30 min
• Lecture4.13
  P-Value and Z-Score Method 30 min
• Lecture4.14
  T-Test, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA) 30 min
• Lecture4.15
  Regression analysis in ANOVA 30 min
• Lecture4.16
  Problem solving for C.L.T Problem solving Hypothesis Testing Problem solving for T-test, Z-score test Copy Copy 30 min
• Lecture4.17
  Case study and model run for ANOVA, ANCOVA 30 min

Hypothesis Testing 6

• Lecture5.1
  Hypothesis Testing Basics 30 min
• Lecture5.2
  Null Hypothesis 30 min
• Lecture5.3
  Alternate Hypothesis 30 min
• Lecture5.4
  p-Value 30 min
• Lecture5.5
  Type of test and Rejection Region 30 min
• Lecture5.6
  Type o errors-Type 1 Errors, Type 2 Errors P value method, Z score Method 30 min

Introduction to Machine Learning 6

UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING

• Lecture6.1
  What is Machine Learning? 30 min
• Lecture6.2
  Introduction to Supervised Learning, Unsupervised Learning & Semi-supervised Learning 30 min
• Lecture6.3
  What is Reinforcement Learning? 30 min
• Lecture6.4
  Variable Identification 30 min
• Lecture6.5
  Missing values treatment 30 min
• Lecture6.6
  Outliers treatment 30 min

Linear Regression 6

• Lecture7.1
  Introduction to Linear Regression Linear Regression with Multiple Variables 30 min
• Lecture7.2
  Disadvantage of Linear Models Interpretation of Model Outputs Understanding 30 min
• Lecture7.3
  Multi-colinearity 30 min
• Lecture7.4
  Missing & Outlier treatment 30 min
• Lecture7.5
  Understanding Heteroscedasticity 30 min
• Lecture7.6
  Case Study – Application of Linear Regression for CTG data 30 min

Logistic Regression 7

• Lecture8.1
  Introduction to Logistic Regression 30 min
• Lecture8.2
  Binary Logistic Regression 30 min
• Lecture8.3
  Multinomial Logistic Regression 30 min
• Lecture8.4
  Introduce the notion of classification Cost function for logistic regressio 30 min
• Lecture8.5
  Application of logistic regression to multi-class classification. 30 min
• Lecture8.6
  Confusion Matrix, Odd's Ratio and ROC Curve Advantages and Disadvantages of Logistic Regression Copy Copy 30 min
• Lecture8.7
  AIC & BIC

Decision Trees 10

• Lecture9.1
  Decision Tree – C4.5, CART, CHAID 30 min
• Lecture9.2
  How to build decision tree? Understanding CART Model Classification Rules 30 min
• Lecture9.3
  Overfitting Problem Stopping Criteria And Pruning 30 min
• Lecture9.4
  Underfitting 30 min
• Lecture9.5
  Gini Index 30 min
• Lecture9.6
  Informations Gain 30 min
• Lecture9.7
  How to find final size of Trees? Model A decision Tree. 30 min
• Lecture9.8
  MDS Copy 30 min
• Lecture9.9
  Random Forests and Support Vector Machines Interpretation of Model Outputs 30 min
• Lecture9.10
  Case Study – 1 Business Case Study for Kart Model 30 min

Unsupervised Learning 10

• Lecture10.1
  Feature Selection & Feature Extraction 30 min
• Lecture10.2
  Feature Construction 30 min
• Lecture10.3
  Hierarchical Clustering 30 min
• Lecture10.4
  K-Means algorithm for clustering – groupings of unlabeled data points. 30 min
• Lecture10.5
  Principal Component Analysis(PCA) 30 min
• Lecture10.6
  Anomaly Detection 30 min
• Lecture10.7
  Association rules 30 min
• Lecture10.8
  Market Basket Analysis 30 min
• Lecture10.9
  Customer Segmentation 30 min
• Lecture10.10
  Dimensionality reduction on CTG 30 min

1. Python Introduction (PART C – PYTHON PROGRAMMING) 3

• Lecture11.1
  Python background, features
• Lecture11.2
  Installation and Various Python IDEs
• Lecture11.3
  Python vs Other languages

2. Basics 6

• Lecture12.1
  Operators in Python – Arithmetic, Relational, Logical and Assignment Operators
• Lecture12.2
  Variables, Types Of Variables
• Lecture12.3
  Naming conventions• Naming conventions
• Lecture12.4
  String operations
• Lecture12.5
  Conditional statements:
• Lecture12.6
  Loops i) For Loops ii) While Loops iii) Break, Continue, Pass, Switch

3. Data Structures 5

• Lecture13.1
  Lists
• Lecture13.2
  Tuples
• Lecture13.3
  Sets
• Lecture13.4
  Dictionaries
• Lecture13.5
  Comprehensions

4. Functions and Modules 6

• Lecture14.1
  Importing a module and setting path
• Lecture14.2
  Creating user defined modules
• Lecture14.3
  Overview of standard python modules
• Lecture14.4
  Function Overview – Defining a function and syntax
• Lecture14.5
  Calling a function and passing variables
• Lecture14.6
  Variables scope, call by value and call by reference

Functional programming 4

• Lecture15.1
  Map
• Lecture15.2
  Reduce
• Lecture15.3
  Filter
• Lecture15.4
  Lambda functions

6. File Handling and external integrations 5

• Lecture16.1
  File operations
• Lecture16.2
  Exception handling using try, except, finally
• Lecture16.3
  Database access
• Lecture16.4
  Web scrapping
• Lecture16.5
  REST API calls

7. Python for Data Science 6

• Lecture17.1
  • Numerical Python
• Lecture17.2
  a) nd array b) Subset, slicing c) Indexing d) List vs nd array e) Manipulating arrays f) Mathematical operations and apply functions g) Linear algebra operations Copy
• Lecture17.3
  • Pandas
• Lecture17.4
  a) Data loading b) Series and Data frame c) Selecting rows and columns d) Position and label-based indexing e) Slicing and dicing f) Merging and concatenating g) Grouping and summarizing h) Lambda functions and pivot tables i) Data Processing, cleaning j) Missing Values k) Outliers Copy
• Lecture17.5
  • Data visualization
• Lecture17.6
  a) Introduction to Matplotlib Basic plotting Figures and sub plotting Box plot, Histograms, Scatter plots, image loading b) Introduction to Seaborn Histogram, rugged plot, hex plot and density plot Joint plot, pair plot, count plot, Heatmaps c) Plotting categorical data and aggregation of values d) Plotting Time-Series data using tsplot Copy