Data Science Training in Hyderabad

1. Fundamentals of Data Science and Machine Learning

• 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
  • Vectors
  • Matrices
• Optimization
  • Theory Of optimization
  • Gradients Descent

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
  • Normal Distribution
  • Binomial Distribution

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. MatPlotLib & Seaborn

• Basics of Plotting
• Plots Generation
• Customization
• Store Plots

8. SciKit Learn

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

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

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

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

• 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