1 Introduction to Python - print(), input(), Comments, Variables, Built-in Data Types

2 Basics of Python - Strings and its methods, Python Booleans, Operators (Arithmetic, Logical, Comparison, Assignment,

Identity, Membership, Bitwise), Slicing and Indexing

3 Python Data Structures

  3.1 Lists - Access, Change, Add, Remove, Loop, Sort, Copy, Join, List Methods, List Comprehension

  3.2 Tuples - Access, Update, Unpack, Loop, Join, Tuple Methods

  3.3 Sets - Access, Add, Remove, Loop, Join, Set Methods

  3.4 Dictionary - Access, Change, Add, Remove, Loop, Copy, Nested Dictionaries, Dictionary Methods

4 Python Conditional Statements - IF, ELIF, ELSE

5 Loops in Python

  5.1 For Loops - Introduction, break, pass, Nested Loops

  5.2 While Loops - Break, Continue, Else, Conditional Statements in While Loops

6 Python Functions - Creating, Calling, Arguments, Arbitrary Arguments, Keyword Arguments, Arbitrary Keyword Arguments,

Positional Arguments, Default Parameters, RETURN, LAMBDA

7 Python Classes and Objects

  7.1 Introduction - Creation, __init__(), __str__(), Object Methods, SELF Statement, Pass Statement

  7.2 Inheritance - Creation, Parent Class, Child Class, __init__(), super(), Inheritance Methods

8 Miscellaneous - Datetime, RegEx, String Formatting, TRY/EXCEPT

1 NumPy - Numerical Python
   1.1 Introduction - Arrays (Indexing, Dimensions, Slicing, Shape, Reshape, Iteration, Joins, Split, Search, Sort, Filter, Copy vs. view
   1.2 Random - Introduction, Pseudo vs True Random, Shuffle and Permutations, Random Distributions (Poisson, Normal,
Binomial, etc.)
   1.3 Universal Functions - Simple Arithmetic, Rounding Decimals, Log, Summation, Product, Differences, LCM, GCD, Set
Operations, Trigonometric and Hyperbolic Functions
2 Data Wrangling using PANDAS
   2.1 Introduction to Pandas - Installation (using PIP) and Import
   2.2 Pandas Series and DataFrames - Difference, Labels, Key/Value Objects
   2.3 Pandas DataFrames Tutorial - View, Info, Description, Location of Rows and Columns, Named Indexes, CSV/EXCEL Files
   2.4 Pandas DataFrames Advanced Tutorial - Cleaning of Data, Handling Null Values (Dropna/Fillna), Handling Duplicate
Values, Handling Data in Wrong Format
   2.5 Plotting of Data Using Pandas
3 Data Visualization Using Matplotlib and Seaborn
   3.1 Introduction to Data Visualization - Installation (using PIP) and import of Matplotlib and Seaborn, Basics of Plotting
(Labels, X Axis, Y Axis, Headings)
   3.2 Matplotlib and Seaborn - Line Plots, Bar Graphs, Scatter plots, Pie Charts, Heatmaps for Correlation, etc.
   3.3 Exploring Outliers and Data Distributions - Boxplots, Histograms, KDE Plot, Violin Plot, Pair Plot, etc.
   3.4 Advanced Plotting Tutorial - Adding a third Axis on a 2D plot, Fonts, Styles, Subplots, Axis, Grids, Texts on Plots
4 Web Scraping using Beautiful Soup
   4.1 Introduction to Web Scraping and Beautiful Soup - Installation and Importing
   4.2 Scrape HTML Content from a Page, Parse HTML Code with BeautifulSoup, Find Elements by ID, Find Elements by HTML
Class Names, Extract Texts, Identify Errors, Extract Attributes from HTML Elements
   4.3 Building a Script using Web Scraping

1. Introduction to Statistics - Data Types: Numeric (Continuous, Discrete), Categorical (Binary, Ordinal, Nominal), Rectangular Data, Non-Rectangular Data
2. Descriptive Statistics
    2.1 Estimates of Location (Mean, Weighted Mean, Trimmed Mean, Median, Weighted Median, Mode, Outliers)
    2.2 Estimates of Variability (Deviations, Variance, Standard Deviation, Mean Absolute Deviation, Median Absolute Deviation, Range, Percentiles, Quantiles, Deciles, Interquartile Range, Degrees of Freedom) 4 Power BI for Data Science
    2.3 Skewness and Kurtosis
3. Sampling Techniques - Bias Sample, Population, Random Sampling, Stratified Sampling, Simple Random Sampling, Bootstrap,
Resampling
4. Inferential Statistics - Confidence Intervals, Normal Distribution (Z-score, QQ-Plot), T-Distrubtion and T-test, Binomial Distribution, Chi-Square Distribution and Chi-Square Test, F-Distribution, F-test, ANOVA Test, Poisson Distribution, Exponential Distribution, Weibull Distribution
5. A/B Testing (Treatment Group, Control Group), Hypothesis Testing (Type 1 Error, Type 2 Error, Significance Value (Alpha)), Permutation Tests, Degrees of Freedom, and Statistical Significance using P-values
6. Correlation Coefficient, Coefficient of Determination, Simple Linear Regression in Statistics

1 Advanced Tutorial on Microsoft Excel

  1.1 Introduction to Excel - Formatting, Insertion, Basic Functions (SUM, AVG, etc.)

  1.2 Pivot Tables and LOOKUP Functions ( VLOOKUP, HLOOKUP, XLOOKUP, etc.)

  1.3 Logical and Statistical Functions

  1.4 Chart Data Techniques

  1.5 Date/Time, Text, Math Functions

  1.6 Advanced Filtering and Sorting

  1.7 Summarizing, Importing and Exporting Data from Databases and Web

2 Structured Query Language

  2.1 Introduction to Databases - (What are Databases), (What is MySQL), (What is RDBMS), (RDBMS v/s NoSQL)

  2.2 Data Base Workflows - Understanding Entity Relationship Diagram, Understanding Normalization (1NF, 2NF, 3NF, BCNF)

  2.3 Structured Query Language (SQL) - CRUD Operations

  2.4 Data Aggregation Functions - (GroupBy), (OrderBy), (HAVING), (COUNT, SUM, MIN, MAX, AVG)

  2.5 Joins in SQL - Primary Key and Foreign Key, Constraints, Set Operations, DML - Savepoint, Rollback

3 Tableau for Data Science

  3.1 Installation, Setup, Importing CSV and Excel Files, Connecting SQL Databases and Cloud Services

  3.2 Data Cleaning and Wrangling - Handling Missing Values, Handling Duplicates, Formatting of Data, Joins in Tableau

  3.3 Basic Visualizations - Bar Chart, Line Chart, Pie Chart, Scatter Plot, Geographical Data Visualization on Maps, Dashboards

in Tableau

  3.4 Advanced Visualizations - Heat Maps, Tree Maps, Boxplots, Histograms, Parameters for interactivity and flexibility, Calculated Fields to create new metrics and dimensions

  3.5 Analytics and Statistical Tools - Trend Lines and Forecasting, Clustering Techniques and Distribution Analysis

  3.6 Filters, Highlighters, Actions to create interactive dashboards, Dashboard Designs

  3.7 Case Study - Real World use case of Tableau for Data Science

4. Power BI for Data Science

  4.1 Installation, Setup, Importing Files, Connecting SQL Databases and Cloud Services, Direct Query Methods in Power BI

  4.2 Power Query - Data Cleaning and Transformation (Handling Missing Values and Duplicates, Formatting of Data)

  4.3 Data Modeling - Calculated Columns, Managing Data Models, Creating Relationship between two tables

  4.4 Basic and Advanced Data Analysis Expressions (DAX) Tutorial

  4.5 Basic and Advanced Visualizations - (Basic - Bar Charts, Pie Charts, Matrices, Maps, etc.), (Advanced - Custom

Visualizations, Slicers, Filters, Waterfall Charts, Funnel Charts, Gauge Charts, etc.)

  4.6 Automated Quick Insights and AI Visuals, Dashboard Designs in Power BI

  4.7 Case Study - Real World use case of Power BI for Data Science

1.1 What is ML
1.2 Why ML
1.3 Types of ML
1.4 Main Challenges - Overfitting, Underfitting, Poor Quality data, Irrelevant Features etc
1.5 What are Hyperparameters
1.6 How to Select ML model 

2.1 Accuracy
2.2 Recall
2.3 Precision
2.4 F1 Score
2.5 Confusion Matrix
2.6 Classification Report
2.7 Precision/Recall Tradeoff
2.8 ROC Curve
2.9 AOC Curve 
2.10 Binary and Multilabel Classification
2.11 Feature Engineering and Feature Importance/Selection 

 3.1 Gradient Descent and Stochastic Gradient Descent
 3.2 Logistic Regression
 3.3 K Nearest Neighbors
 3.4 Naive Bayes
 3.5 Support Vector Machines
 3.6 Linear Discriminant Analysis
 3.7 Decision Trees
 3.8 Hyperparameter Tuning - GridSearchCV and RandomizedSearchCV

4.1 Bagging - Eg: Voting Classifiers
4.2 Boosting - XG Boost, Adaboost, etc
4.3 Cross-Validation
4.4 Random Forest Classifier
4.5 XG Boost Classifier
4.6 Stacking
4.7 Hyperparameter Tuning 

5.1 Simple Linear Regression
5.2 Multiple Linear Regression
5.3 Polynomial Regression
5.4 Cost Function and Gradient Descent
5.5 Performance Metrics - MSE, RMSE, MAE etc
5.6 Heteroskedasticity, Normality and Correlated Errors
5.7 Hyperparameter Tuning

 6.1 Decision Tree Regressor
 6.2 Support Vector Machines
 6.3 K Nearest Neighbors
 6.4 Random Forest
 6.5 Boosting
 6.6 Hyperparameter Tuning  

7.1 Introduction to Unsupervised Learning
7.2 K Means Clustering
7.3 Hierarchical Clustering 
7.4 Model-Based Clustering
7.5 DBSCAN
7.6 Anamoly Detection using Gaussian Mixtures

Dimensionality Reduction - Principal Component Analysis (1 class on this topic )

Recommendation Systems (2 Class on this Topic )