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

Learners

3 - 4 Months

Recommended 3-5 hrs/week

Sept 7,2023

Start Date

India's #1 Techno Company

Most Demanded Job Roles

Increase in Demand of
Data Scientists in 2022

47.1%

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Increase in Demand of
Data Analysts in 2022

27.9%

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Increase in Demand of
AI & Machine Learning in 2022

40%

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Certification Course in Data Science Get the Beta Effect

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Get personalised mentoring with industry experts

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

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1200

User Enrolled

7th Sept

Batch Starts

3-4 Months

Program Duration

Online

Mode

Learning Path

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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

Numpy

    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

Key Features

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Essential Data Science Skills

Essential Data Science Skilld

Tools in Data Science

Anaconda

Anaconda

Python

Python

Pandas

Pandas

Numpy

Numpy

Matplotlib

Matplotlib

Power BI

Power BI

AWS

AWS

SQL

SQL

Excel

Excel

Tableau

Tableau

100+ Guided Projects

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IPL Complete Dataset

About Dataset

Now that this year's IPL is over, let's not curb our cricket love and start analyzing the whole of IPL with this latest and complete Indian Premier League dataset. It contains the match descriptions, results, winners, player of the matches, ball by ball dataset and much more. So, stop thinking and start analyzing .

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Data Analytics and Visualisation of UFC

About Dataset

Data and visual analytics is an emerging field concerned with analyzing, modeling, and visualizing complex high dimensional data. This course will introduce students to the field by covering state-of-the-art modeling, analysis and visualization techniques.

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Population and literacy stats in India

About Dataset

And this weightage is achieved after a very long period of reforms and policies. In the year 1947 when India got its independence, the literacy rate was dismal. Only 18% of the overall population was literate.

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