Best Data Science Training in Bangalore

1. Data Science Overview
2. Data Science
3. Data Scientists
4. Examples of Data Science
5. Python for Data Science

1. Introduction to Python and its usage in the industry
2. Introduction to Anaconda
3. Installation of Anaconda Python Distribution
4. Jupyter Notebook Installation
5. Jupyter Notebook Introduction
6. Variable Assignment
7. Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
8. Creating, accessing, and slicing tuples
9. Creating, accessing, and slicing lists
10. Creating, viewing, accessing, and modifying dicts
11. Creating and using operations on sets
12. Basic Operators: 'in', '+', '*'
13. Functions
14. Lambda functions
15. Object Oriented Programming
16. Regular expression
17. Database programming
18. Sample programs and assignment

1. Introduction to Data Visualization
2. Processes in Data Science
3. Data Wrangling, Data Exploration, and Model Selection
4. Exploratory Data Analysis or EDA
5. Data Visualization
6. Plotting
7. Hypothesis Building and Testing

1. eed ro ad

1. Introduction to Statistics
2. Statistical and Non-Statistical Analysis
3. Some Common Terms Used in Statistics
4. Data Distribution: Central Tendency, Percentiles, Dispersion
5. Histogram
6. Bell Curve
7. Hypothesis Testing
8. Chi-Square Test
9. Correlation Matrix
10. Inferential Statistics

1. NumPy Overview
2. Properties, Purpose, and Types of ndarray
3. Class and Attributes of ndarray Object
4. Basic Operations: Concept and Examples
5. Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
6. Copy and Views
7. Universal Functions (ufunc)
8. Shape Manipulation
9. Broadcasting
10. Linear Algebra

1. Introduction to Pandas
2. Data Structures
3. Series
4. DataFrame
5. Missing Values
6. Data Operations
7. Data Standardization
8. Pandas File Read and Write Support
9. SQL Operation

1. Introduction to Data Visualization
2. Python Libraries
3. Plots
4. Matplotlib Features:
   • Line Properties Plot with (x, y)
   • Controlling Line Patterns and Colors
   • Set Axis, Labels, and Legend Properties
   • Alpha and Annotation
   • Multiple Plots
   • Subplots
5. Types of Plots and Seaborn

1. Introduction to Machine Learning
2. Machine Learning Approach
3. How Supervised and Unsupervised Learning Models Work
4. Scikit-Learn
5. Machine Learning Algorithms o Linear Regression o Logistic Regression o Decision Tree o Support Vector Machine (SVM) o Naive Bayes o K Nearest Neighbour (KNN) o K-Means o Random Forest o Dimensionality Reduction Algorithms
6. Model Persistence
7. Model Evaluation - Metric Functions

1. NLP Overview
2. NLP Approach for Text Data
3. NLP Environment Setup
4. NLP Sentence analysis
5. NLP Applications
6. Major NLP Libraries
7. Scikit-Learn Approach
8. Scikit - Learn Approach Built - in Modules
9. Scikit - Learn Approach Feature Extraction
10. Bag of Words
11. Extraction Considerations
12. Sentimental analysis

Finally: time to do the project and take online assesment test