1. Fundamentals of Data Science and Mathematical statistics
    ● Introduction to Data Science
    ● Need of Data Science
    ● BigData and Data Science
    ● Data Science and machine learning
    ● Data Science Life Cycle
    ● Data Science Platform
    ● Data Science Use Cases
    ● Skill Required for Data Science
2. Mathematics For Data Science
    ● Linear Algebra-Matrices
        o Zero
        o One
        o Identify
        o Diagonal
        o Column
        o Row
        o Operations
3. Introduction to Statistics
    ● Descriptive vs. Inferential Statistics
    ● Types of data
    ● Measures of central tendency and dispersion
    ● Hypothesis & inferences
    ● Hypothesis Testing
    ● Confidence Interval
    ● Central Limit Theorem
4. Probability and Probability Distributions
    ● Probability Theory
    ● Conditional Probability
    ● Data Distribution
    ● Distribution Functions
        o Normal Distribution
        o Binomial Distribution

1. Using a Spreadsheet
    ● What is Excel?
    ● Why Use Excel?
    ● Excel Overview
    ● Excel Ranges, Selection of Ranges
    ● Excel Fill, Fill Copies, Fill Sequences,Sequence of Dates
    ● Excel adds, move, and delete cells
    ● Excel Formulas
    ● Relative and Absolute References
2. Functions
    ● SUM
    ● AVERAGE
    ● COUNT
    ● MAX & MIN
    ● RANDBETWEEN
    ● TRIM
    ● LEN
    ● CONCATENATE
    ● TODAY & NOW
3. Advanced Functions
    ● Excel IF Function
    ● Excel If Function with Calculations
    ● How to use COUNT, COUNTIF, and
    ● COUNTIFS Function?
4. Data Visualization
    ● Excel Data Analysis – Data Visualization
    ● Visualizing Data with Charts
    ● Chart Elements and Chart Styles
    ● Data Labels
    ● Quick Layout

  ● An Introduction to RDBMS & SQL
    ● Data Retrieval with SQL
    ● Pattern matching with wildcards
    ● Basics of sorting
    ● Order by clause
    ● Aggregate functions
    ● Group by clause
    ● Having clause
    ● Nested queries
    ● Inner join
    ● Multi join
    ● Outer join
    ● Adding and Deleting columns
    ● Changing column name and Data Type
    ● Creating Table from existing Table
    ● Changing Constraints Foreign key

1. An Introduction to Python

    ● Why Python , its Unique Feature and where to use it?
    ● Python environment Setup/shell
    ● Installing Anaconda
    ● Understanding the Jupyter notebook
    ● Python Identifiers, Keywords
    ● Discussion about installed module s and packages
2. Conditional Statement ,Loops and File Handling
    ● Python Data Types and Variable
    ● Condition and Loops in Python
    ● Decorators
    ● Python Modules & Packages
    ● Python Files and Directories manipulations
    ● Use various files and directory functions for OS operations
3. Python Core Objects and Functions
    ● Built in modules (Library Functions)
    ● Numeric and Math’s Module
    ● String/List/Dictionaries/Tuple
    ● Complex Data structures in Python
    ● Python built in function
    ● Python user defined functions
4. Introduction to NumPy
    ● Array Operations
    ● Arrays Functions
    ● Array Mathematics
        ▪ Mean
        ▪ Standard Deviation
        ▪ Max
        ▪ Min
    ● Array Manipulation
        ▪ Reshaping
        ▪ Resizing
    ● Array I/O
    ● Matrix creation
    ● Data generation
    ● Random function
    ● Normalized Data Generation
    ● Indexing and Slicing
    ● Transpose
    ● Importing Files with Numpy
5. Data Manipulation with Pandas
    ● Data Frames
    ● Series
    ● I/O
    ● Creating Pandas DataFrame
    ● Selection in DFs
    ● Data Describe
    ● Data info
    ● Retrieving in DFs
    ● Applying Functions
    ● Reshaping the DFs – Pivot
    ● Combining DFs Merge Join
    ● Data Alignment
6. SciPy
    ● Hypothesis Testing using Scipy
    ● Shapiro Test
    ● Spearmaman Test
    ● T-Test of Independents
    ● Chi-Square Test
7. Visualization with Seaborn
    ● Seaborn Installation
    ● Introduction to Seaborn
    ● Basics of Plotting
    ● Plots Generation
    ● Visualizing the Distribution of a Dataset
    ● Selection color palettes
8. Visualization with Matplotlib
    ● Matplotlib Installation
    ● Matplotlib Basic Plots & it’ s Containers
    ● Matplotlib components and properties
    ● Scatter plots
    ● 2D Plots
    ● Histograms
    ● Bar Graphs
    ● Pie Charts
    ● Box Plots
    ● Customization
    ● Store Plots
9. Descriptive Statistics
    ● Data understanding
    ● Observations, variables, and data matrices
    ● Types of variables
    ● Measures of Central Tendency
    ● Arithmetic Mean / Average
        ▪ Merits & Demerits of Arithmetic Mean and Mode
        ▪ Merits & Demerits of Mode and Median
        ▪ Merits & Demerits of Median Variance
10. Probability Basics
    ● Notation and Terminology
    ● Unions and Intersections
    ● Conditional Probability and Independence
11. Probability Distributions
    ● Random Variable
    ● Parameters vs. Statistics
    ● Binomial Distribution
    ● Central Limit Theorem
12. Tests of Hypothesis
    ● Large Sample Test
    ● Small Sample Test
    ● One Sample: Testing Population Mean
    ● Hypothesis in One Sample z-test
    ● Two Sample: Testing Population Mean
    ● One Sample t-test – Two Sample t-test
    ● Paired t-test
    ● Hypothesis in Paired Samples t-test
    ● Chi-Square test
13. Data Analysis
    ● Case study- Netflix
    ● Deep analysis on Netflix data

1. Exploratory Data Analysis
    ● Data Exploration
    ● Missing Value handling
    ● Outliers Handling
    ● Feature Engineering
    ● Train-Test Split
    ● Standard Scaler
    ● Min-Max Scaler
    ● Data Preprocessing
    ● Resampling
        ▪ Up-Sampling
        ▪ Down-Sampling
2. Machine Learning: Supervised Algorithms Classification
    ● Introduction to Machine Learning
    ● Linear Regression
    ● Multiple Linear Regression
    ● Gradient Descent
    ● Logistic Regression
        ▪ Binary Classification
        ▪ Multiclass classification
    ● Naïve Bays Algorithm
    ● K-Nearest Neighbor Algorithm
    ● Decision Tress
        1. Classification
        2. Regression
    ● Support Vector Machines
    ● Random Forest
    ● Model Evaluation and performance
        ▪ Accuracy ,Precision
        ▪ Recall
        ▪ F1 Score
        ▪ Confusion Matrix
        ▪ Classification Report
        ▪ K-Fold Cross Validation
        ▪ ROC, AUC etc…
    ● Hyper parameter tuning
3. Machine Learning: Unsupervised Learning Algorithms
    ● Similarity Measures
    ● Cluster Analysis and Similarity Measures
    ● K-Means Clustering
        ▪ Elbow Method
    ● Hierarchical Clustering
        ▪ Agglomerative Clustering
    ● K-Nearest Neighbors
4. Ensemble algorithms
    ● Bagging
        ▪ Random Forest
    ● Boosting
        ▪ Adaboost
    ● Voting
    ● Stacking
    ● Principal Components Analysis
5. Recommendation Systems
    ● Collaborative filtering model
    ● Content-based filtering model.
    ● Hybrid collaborative system

1. Artificial Intelligence
    ● An Introduction to Artificial Intelligence
    ● History of Artificial Intelligence
    ● Future and Market Trends in AI
    ● Intelligent Agents – Perceive-ReasonAct Loop
    ● Constraint-based Reasoning
2. Deep networks and structured knowledge
    ● Studying Neural Elements
    ● Neural Networks and Perceptions
    ● Understanding Feedforward Networks
    ● Exploring Backpropagation
    ● Working of Neural Networks
3. Natural Language Processing
    ● Natural Language Processing
    ● NLP in Python
    ● Tokenization
    ● Part of Speech Tagging (POS Tagging)
    ● Named Entity Recognition
    ● Semantic Analysis
    ● Sentiment Analysis
4. Artificial Neural Network
    ● Understanding Artificial Neural Network
    ● The Activation Function
    ● Building an ANN
    ● Building Problem Description
    ● Evaluation the ANN
    ● Improving the ANN
    ● Tuning the ANN.
5. Conventional Neural Networks
    ● CNN Intuition
    ● Convolution Operation
    ● Kernel Operation
    ● Filtering operation
    ● Padding on image
    ● Pooling Layer
    ▪ Max Pooling
    ▪ Average Pooling
    ● ReLU Layer
    ● Fully Connected Dense Layer
    ● Softmax and Cross-Entropy
    ● Building a CNN
    ● Evaluating the CNN
    ● Improving the CNN
    ● Tuning the CNN
6. Recurrent Neural Network
    ● Recurrent Neural Network
    ● RNN Intuition
    ● The Vanishing Gradient Problem
    ● LSTMs and LSTM Variations
    ● Practical Intuition
    ● Building an RNN
    ● Evaluating the RNN
    ● Improving the RNN
    ● Tuning the RNN
7. Time Series Data
    ● Introduction to Time series data
    ● Data cleaning in time series
    ● Pre-Processing Time-series Data
    ● Prediction in Time Series using LSTM
    ● Prediction in Time Series using ARIMA

1. Introduction to Git & Distributed Version Control
2. Life Cycle
3. Create clone & commit Operations
4. Push & Update Operations
5. Stash, Move, Rename & Delete Operations.

1. Machine Learning Features & Services
    ● Using python in Cloud
    ● Understanding Google Colab
    ● Understanding Jupyter Lab
    ● How to access Machine Learning Services
    ● Lab on accessing Machine learning services
    ● Uploading Data
    ● Preparation of Data
    ● Applying Machine Learning Model

1. Introduction to Data Visualization & Power of Tableau
    ● Architecture of Tableau
    ● Product Components
    ● Working with Metadata and Data Blending
    ● Data Connectors
    ● Data Model
    ● File Types
    ● Data Source Filters
2. Scatter Plot
    ● Funnel Chart
    ● Waterfall Chart
    ● Working with Filters
    ● Organizing Data and Visual Analytics
    ● Working with Mapping
    ● Working with Calculations and Expressions
    ● Working with Parameters
    ● Charts and Graphs
    ● Dashboards and Stories
Data Visualization with PowerBI
3. Introduction to Data Visualization & Power of Tableau
    ● Architecture of Tableau
    ● Product Components
    ● Working with Metadata and Data Blending
    ● Data Connectors
    ● Data Model
    ● File Types
    ● Data Source Filters
4. Scatter Plot
    ● Funnel Chart
    ● Waterfall Chart
    ● Working with Filters
    ● Organizing Data and Visual Analytics
    ● Working with Mapping
    ● Working with Calculations and Expressions
    ● Working with Parameters
    ● Charts and Graphs

Project Work and Case Studies
    ❖ Machine Learning end to end Project blueprint
    ❖ Case study on real data after each model.
    ❖ Regression predictive modeling – E-commerce
    ❖ Classification predictive modeling – Binary Classification
    ❖ Case study on Binary Classification – Bank Marketing
    ❖ Case study on Sales Forecasting and market analysis
    ❖ Widespread coverage for each Topic
    ❖ Various Approaches to Solve Data Science Problem
    ❖ Pros and Cons of Various Algorithms and approaches
    ❖ Amazon-Recommender
    ❖ Image Classification Sentiment Analysis
Project Domain: FINANCE
    ● The insurance company wants to decide on the premium using various parameters of the client.
    ● It’ s an important problem to keep the clients and attract new ones.
By completing this Project you will learn:
    ● How to collect data?, how to justify the right features? , Which ML / DL model is best in this situation? How much data is enough?
    ● How to have CI/CD in the project?
    ● How to do Deployment of Project to cloud?
Project Domain: Image Processing in Health care
    ● A hospital wants to automate the Detection of pneumonia in X-rays using image processing.
By completing this Project you will learn:
    ● How to handle image data? How to preprocess and augment image data?
    ● How to choose the right model for the image process?
   ● How to apply transfer learning in image processing?
    ● How to do incremental learning & CI/CD in the project?
    ● How to do Deployment of Project to cloud?

Natural Language Processing
    ● One of the companies wants to automate applicant’ s level in English communication.
    ● Create a ML/DL model for this task.
By completing this Project you will learn:
    ● How do convert text to the right representation?
    ● How to preprocess text data? How to select the right ML/DL model for text data?
    ● How to do transfer learning in Text Analytics?
    ● How to do CI/CD in a text analytics project? How to do Deployment of Project to cloud?
Mechanical
    ● A mechanical company wants to perform predictive maintenance of engine parts.
    ● This enables the company to efficiently change parts before the machine fails.
By completing this Project you will learn:
    ● How to handle time-series data?
    ● How to preprocess time series data?
    ● How to create ML/DL model for Time-series Data?
    ● How to do CI/CD in a text analytics project?
    ● How to do Deployment of Project to cloud?
Sales / Demand Forecasting
    ● Predict the sales/demand of a product of a company.
    ● Sales / Demand forecasting of the product will help the company efficiently manage the resources.
    ● Create a ML/DL model for this problem.
By completing this Project you will learn:
    ● How to handle time-series data?
    ● How to preprocess time series data?
    ● How to create ML/DL model for Time-series Data?
    ● How to do CI/CD in a text analytics project? How to do Deployment of Project to cloud?