Certification Course in
Data Science

Kick-start your journey in Data Science & Machine Learning with India's best selling advanced certification Programme from TalentServe through Hands on Projects, Cutting Edge Curriculum, & Mentorship.

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1 Lac


3 - 4 Months

Recommended 3-5 hrs/week

Sept 7,2023

Start Date

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Data Science refers to the process of extracting valuable information from various structured or unstructured data.

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User Enrolled

7th Sept

Batch Starts

3-4 Months

Program Duration



Learning Path


Modules of Data Science Certification

Part I

1. Module

Introduction to Python

  • Features, the advantages of Python over other programming languages · Python installation – Windows, Mac & Linux distribution for Anaconda Python

  • Deploying Python IDE

  • Basic Python commands, data types, variables, keywords and More

2. Module

Control Flow and Looping

  • Conditional statements (if, else, elif)

  • Nested conditionals

  • Logical operators (and, or, not)

  • Loops (for, while) and their applications

  • Control flow manipulation (break, continue)

3. Module

Data Structures

  • Lists: creating, indexing, slicing, and modifying
  • Tuples: creating, accessing elements, and immutability
  • Dictionaries: creating, accessing values, and key-value pairs
  • String manipulation: string methods, formatting, and common operations
  • File handling: reading and writing files in Python

4. Module


    Functions and Modules
  • Defining functions with parameters and return values
  • Function scope and global variables
  • Lambda functions
  • Importing modules and libraries in Python
  • Creating custom modules and using them

5. Module

Numpy and Pandas

  • Introduction to numerical computing with Numpy
  • Numpy arrays: creating, indexing, slicing, and basic operations
  • Mathematical operations and functions with Numpy
  • Introduction to Pandas and its data structures (Series and DataFrame)
  • Data manipulation with Pandas: indexing, filtering, and sorting

6. Module

Data Visualization with Matplotlib

  • Introduction to data visualization
  • Creating basic plots: line plots, scatter plots, bar plots, and histograms
  • Customizing plots: adding titles, labels, and legends
  • Plotting with Pandas and Matplotlib
  • Saving plots to files

7. Module

Data Analysis with Pandas

  • Data aggregation and grouping in Pandas
  • Combining and merging datasets
  • Handling missing data in Pandas
  • Data cleaning and preprocessing techniques
  • Applying functions to data in Pandas

Part II

8. Module

Advanced Data Visualization

  • Advanced plotting techniques with Matplotlib
  • Plotting multiple subplots and custom layouts
  • Visualizing relationships between variables: scatter matrices, heatmaps
  • Interactive visualizations with tools like Seaborn and Plotly
  • Overview of other data visualization libraries and their applications

9. Module

Data Wrangling and Cleaning

  • Understanding the data acquisition process
  • Data cleaning techniques (handling missing data, data imputation)
  • Data transformation (merging, reshaping, and filtering datasets)
  • Data normalization and scaling

10. Module

Exploratory Data Analysis (EDA)

  • Exploring datasets using descriptive statistics
  • Identifying patterns and relationships in data
  • Feature selection and engineering

11. Module

Machine Learning Fundamentals

  • Introduction to supervised, unsupervised, and reinforcement learning
  • Train-test split and cross-validation for model evaluation
  • Regression techniques (Linear Regression, Polynomial Regression)
  • Classification algorithms (Logistic Regression, Decision Trees, Random Forests)

12. Module

Unsupervised Learning and Clustering

  • Clustering algorithms (K-Means, Hierarchical Clustering)
  • Dimensionality reduction techniques (PCA, t-SNE)
  • Anomaly detection and novelty detection
  • Evaluating unsupervised learning models

13. Module

Model Evaluation and Optimization

  • Model evaluation metrics (accuracy, precision, recall, F1-score)
  • Hyperparameter tuning and model optimization
  • Handling imbalanced datasets
  • Ensemble methods (Bagging, Boosting)

14. Module

Introduction to Deep Learning

  • Neural networks and their components
  • Building neural networks using Keras or PyTorch
  • Deep learning for image recognition and natural language processing
  • Transfer learning and fine-tuning pre-trained models

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