Data Science Online Certification

• Vectors
• Matrices

• Theory Of optimization
• Gradients Descent

• Normal Distribution
• Binomial Distribution

• An Introduction to RDBMS & SQL
• Data Retrieval with SQL
• Pattern matching with wildcards
• Basics of sorting
• Session summary
• 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
• Introduction to Stored Procedures.

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
• Array Manipulation
• Array I/O
• Importing Files with Numpy

5. Data Manipulation with Pandas

• Data Frames
• I/O
• Selection in DFs
• Retrieving in DFs
• Applying Functions
• Reshaping the DFs – Pivot
• Combining DFs
  Merge
  Join
• Data Alignment 

6. SciPy

• Matrices Operations
• Create matrices
  Inverse, Transpose, Trace,   Norms , Rank etc
• Matrices Decomposition
  Eigen Values & vectors
  SVDs

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
• Pylab & Pyplot
• Scatter plots
• 2D Plots-
• Histograms
• Bar Graphs
• Pie Charts
• Box Plots
• Customization
• Store Plots

9. SciKit Learn

• Basics
• Data Loading
• Train/Test Data generation
• Preprocessing
• Generate Model
• Evaluate Models

10. 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

11. Probability Basics

• Notation and Terminology
• Unions and Intersections
• Conditional Probability and Independence

12. Probability Distributions

• Random Variable
• Probability Distributions
• Probability Mass Function
• Parameters vs. Statistics
• Binomial Distribution
• Poisson Distribution
• Normal Distribution
• Standard Normal Distribution
• Central Limit Theorem
• Cumulative Distribution function

13.  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

14. Data Analysis

• Case study- Netflix
• Deep analysis on Netflix data

• 1. Exploratory Data Analysis
  • Data Exploration
  • Missing Value handling
  • Outliers Handling
  • Feature Engineering
  2. Feature Selection
  • Importance of Feature Selection in Machine Learning
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods
  3. Machine Learning: Supervised Algorithms Classification
  • Introduction to Machine Learning
  • Logistic Regression
  • Naïve Bays Algorithm
  • K-Nearest Neighbor Algorithm
  • Decision Tress
    1. SingleTree
    2. Random Forest
  • Support Vector Machines
  • Model Ensemble
  • Model Evaluation and performance
    • K-Fold Cross Validation
    • ROC, AUC etc…
  • Hyper parameter tuning
    • Regression
    • classification
  4. Machine Learning: Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree and Random Forest Regression
  5. Machine Learning: Unsupervised Learning Algorithms
  • Similarity Measures
  • Cluster Analysis and Similarity Measures
  6. Ensemble algorithms
  • Bagging
  • Boosting
  • Voting
  • Stacking
  • K-means Clustering
  • Hierarchical Clustering
  • Principal Components Analysis
  • Association Rules Mining & Market Basket Analysis 

  7. Recommendation Systems

  • collaborative filtering model
  • content-based filtering model.
  • Hybrid collaborative system.

• • Introduction to Git and Distributed version control
  • Life Cycle
  • Create clone & commit Operations
  • Push & Update Operations
  • Stash, Move, Rename & Delete Operations
  •  

• 1. Artificial Intelligence
     • An Introduction to Artificial Intelligence
     • History of Artificial Intelligence
     • Future and Market Trends in Artificial Intelligence
     • Intelligent Agents – Perceive-Reason-Act Loop
     • Search and Symbolic Search
     • Constraint-based Reasoning
     • Simple Adversarial Search (Game-Playing)
     • Neural Networks and Perceptions
     • Understanding Feedforward Networks
     • Boltzmann Machines and Autoencoders
     • Exploring Backpropagation
  2. Deep Networks and Structured Knowledge
     • Understanding Sensor Processing
     • Natural Language Processing
     • Studying Neural Elements
     • Convolutional Networks
     • Recurrent Networks
     • Long Short-Term Memory (LSTM) Networks
  3. Natural Language Processing
     • Natural Language Processing
     • Natural Language Processing in Python
     • Studying Deep Learning
     • Artificial Neural Networks
     • ANN Intuition
     • Plan of Attack
     • Studying the Neuron
     • The Activation Function
     • Working of Neural Networks
     • Exploring Gradient Descent
     • Stochastic Gradient Descent
     • Exploring Backpropagation
  4. Artificial and Conventional Neural Network
     • Understanding Artificial Neural Network
     • Building an ANN
     • Building Problem Description
     • Evaluation the ANN
     • Improving the ANN
     • Tuning the ANN
  5. Image Processing / Machine Vision
  • Image basics
  • Loading and saving images
  • Thresholding
  • Bluring
  • Masking
  • Image Augmentation
  6. Conventional Neural Networks
  • CNN Intuition
  • Convolution Operation
  • ReLU Layer
  • Pooling and Flattening
  • Full Connection
  • Softmax and Cross-Entropy
  • Building a CNN
  • Evaluating the CNN
  • Improving the CNN
  • Tuning the CNN
  7. 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
  8. Time Series Data
  • Introduction to Time series data
  • Data cleaning in time series
  • Pre-Processing Time series Data
  • Predictions in Time Series using ARIMA, Facebook Prophet models.

• Machine Learning Features and Services

  • Using python in Cloud
  • How to access Machine Learning Services
  • Lab on accessing Machine learning services
  • Uploading Data
  • Preparation of Data
  • Applying Machine Learning Model
  • Deployment by publishing Models using  AWS or other cloud computing

• 1. Introduction to Data Visualization and the Power of Tableau

  • Architecture of Tableau
  • Product Components
  • Working with Metadata and Data Blending
  • Data Connectors
  • Data Model
  • File Types
  • Dimensions & Measures
  • Data Source Filters
  • Creation of Sets.

  2. Scatter Plot

  • Gantt Chart
  • 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

• • 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 Domains: Finance

  • 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 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?

   

  Image Processing in Health care

  • A hospital wants to automate Detection of pneumonia in X-rays using image processing.

  By doing this project you will understand

  • How to handle image data? How to preprocess and augment image data? How to choose right model for 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 to do convert text to right representation? How to preprocess text data? How to select right ML/DL model for text data ?
  • How to do transfer learning in Text Analytics?
  • How to do CI/CD in 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 company to efficiently change parts before machine fails.

  By performing this task 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 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 company efficiently manage the resources.
  • Create a ML/DL model for this problem.

  By performing 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 text analytics project?
  • How to do Deployment of Project to cloud?