Data Science Training in Hyderabad
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
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
- No Prerequisites for Data Science certification training
- Basic knowledge of SQL is advantageous
Data Science Course Syllabus
Decade Years Legacy of Excellence | Multiple Cities | Manifold Campuses | Global Career Offers
- 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
- Mathematics For Data Science
- Linear Algebra
- Vectors
- Matrices
- Optimization
- Theory Of optimization
- Gradients Descent
- 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
- Probability and Probability Distributions
- Probability Theory
- Conditional Probability
- Data Distribution
- Distribution Functions
- Normal Distribution
- Binomial Distribution
- 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
- 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
- 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
- Exploratory Data Analysis
- Data Exploration
- Missing Value handling
- Outliers Handling
- Feature Engineering
- Feature Selection
- Importance of Feature Selection in Machine Learning
- Filter Methods
- Wrapper Methods
- Embedded Methods
- Machine Learning: Supervised Algorithms Classification
- Introduction to Machine Learning
- Logistic Regression
- Naïve Bays Algorithm
- K-Nearest Neighbor Algorithm
- Decision Tress
- SingleTree
- Random Forest
- Support Vector Machines
- Model Ensemble
- Model Evaluation and performance
- K-Fold Cross Validation
- ROC, AUC etc…
- Hyper parameter tuning
- Regression
- classification
- Machine Learning: Regression
- Simple Linear Regression
- Multiple Linear Regression
- Decision Tree and Random Forest Regression
- Machine Learning: Unsupervised Learning Algorithms
- Similarity Measures
- Cluster Analysis and Similarity Measures
- 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
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Who can apply for the course?
- Aspiring Data Scientists who are interested in switching careers.
- Graduate/post-graduate students wishing to pursue their careers in Data Analytics/Data Science.
- Professionals who work with big data.
- Professionals from non-IT bkg, and want to establish in IT.
- Candidate who would like to restart their career after a gap.
- Machine learning is a topic of interest to professionals.
- Business analysts and those who work with data
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Industry Projects
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- Project Implementation with Real-Time Scenario.
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