Data Science Training in Hyderabad

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Upgrade your Data Science Skillset with our Data Analyst courses in Hyderabad!

Trained 15000+ Students | Course duration: 40 hours | Real-time Project Execution | Certification exam after course completion | Basic to advanced level learning |

Key Features

Course Duration : 8 Weeks

Live Projects : 1

Online Live Training

EMI Option Available

Certification & Job Assistance

24 x 7 Lifetime Support

Our Industry Expert Trainer

We are a team of 10+ Years of Industry Experienced Trainers, who conduct the training with real-time scenarios.
The Global Certified Trainers are Excellent in knowledge and highly professionals.
The Trainers follow the Project-Based Learning Method in the Interactive sessions.

Overview of Data Science Training Course in Hyderabad

Features of this Data Science course

Data Science Course features

  • Live Sessions
  • Mocks, Assignments, & Tests
  • Job Assistance
  • 24/7 Lifetime Technical Support
  • 10+ years of experience Proficient
  • Real-time project experience
  • Flexible Timings

Prerequisites

Basic knowledge of Python programming language, SQL, and files (MS Excel, CSV, etc.) with knowledge about algebra and geometry.

Course Duration

40 hours, i.e., 8-9 weeks approx.

Who all can apply for this course?

  • Career switch Developers
  • Candidates willing to start their career in Data Science or data analytics field
  • Machine Learning or Hadoop background developers
  • Data Analysts
  • Business Analysts

Skills Required

Certifications
0 +
24x7 Support and Access
24x
40 to 50 Hour Course Duration
40- 0
Extra Activities, Sessions
0 %

Data Science Course Syllabus

Decade Years Legacy of Excellence | Multiple Cities | Manifold Campuses | Global Career Offers

Module 1: Fundamentals of Statistics & Data Science
  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
  1. Mathematics For Data Science
  • Linear Algebra
    • Vectors
    • Matrices
  • Optimization
    • Theory Of optimization
    • Gradients Descent
  1. 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
  1. Probability and Probability Distributions
  • Probability Theory
  • Conditional Probability
  • Data Distribution
  • Distribution Functions
    • Normal Distribution
    • Binomial Distribution
Module 2: Python for Data Science
  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
  1. 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
  1. 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
Module 3: Machine Learning
  1. Exploratory Data Analysis
  • Data Exploration
  • Missing Value handling
  • Outliers Handling
  • Feature Engineering
  1. Feature Selection
  • Importance of Feature Selection in Machine Learning
  • Filter Methods
  • Wrapper Methods
  • Embedded Methods
  1. 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
  1. Machine Learning: Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Decision Tree and Random Forest Regression
  1. Machine Learning: Unsupervised Learning Algorithms
  • Similarity Measures
  • Cluster Analysis and Similarity Measures
  1. Ensemble algorithms
  • Bagging
  • Boosting
  • Voting
  • Stacking
  • K-means Clustering
  • Hierarchical Clustering
  • Principal Components Analysis
  • Association Rules Mining & Market Basket Analysis
Module 4: 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

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