Data Analytics Courses in Kurnool

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. Using Spreadsheet

• What is Excel?
• Why Use Excel?
• Excel Overview
• Excel Ranges,Selection of Ranges
• Excel Fill,Fill Copies,Fill Sequences,Sequence of Dates
• Excel add,move,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?
• Excel Advanced If Functions

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
• 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.

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.

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.

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?