Data Science & Analytics Certificate in Dehradun

Course Description

Acadmiac’s Data Science & Analytics Certificate Program is designed to build strong analytical and problem-solving skills through a practical, data-driven learning approach. The program covers Python programming, data handling, visualization, statistics, exploratory data analysis, and the fundamentals of machine learning. Delivered through structured modules and hands-on projects, the course helps learners understand how data is collected, analyzed, and transformed into actionable insights. By the end of the program, learners gain the technical foundation, analytical mindset, and confidence required to begin their journey in data science and analytics.

The Acadmiac Advantage

Develop a solid foundation in data analysis, statistics, and Python, essential for modern data-driven roles.
Learn to work with real datasets using industry-standard tools and libraries for data handling and visualization.
Build analytical thinking through structured exploration, interpretation, and insight generation.
Apply what you learn through practical projects that reflect real-world data science workflows.

Meaningful careers are built on clarity and capability. This data science program is designed to help learners move beyond theory and develop confidence in working with data. In every module, students learn not only tools and techniques but also how to think analytically and solve problems using data. By the end of the course, learners are prepared to explore entry-level data roles or advance further into specialized data science pathways.

Module 1: Introduction to Data Science & Analytics

Understand what data science is and how data-driven decision-making works.

What is data science and analytics?
Types of data and data sources
Data science lifecycle
Real-world applications of data science

Practical Tasks
Outcome: Understand the scope and role of data science in real-world problems.

Module 2: Python Programming for Data Science

Learn Python fundamentals required for data analysis and processing.

Python basics and syntax
Variables, data types, and operators
Control flow and functions
Working with Python environments

Practical Tasks
Outcome: Write Python programs for data handling and analysis.

Module 3: Data Handling with NumPy & Pandas

Work with structured data using industry-standard Python libraries.

NumPy arrays and operations
Pandas Series and DataFrames
Data loading and inspection
Data cleaning and preprocessing

Practical Tasks

Outcome: Manipulate and clean datasets using Python libraries.

Module 4: Data Visualization & Insights

Visualize data to identify trends, patterns, and insights.

Data visualization concepts
Matplotlib and Seaborn basics
Charts, graphs, and plots
Data storytelling fundamentals

Practical Tasks
Outcome: Create meaningful visualizations to interpret data.

Module 5: Statistics for Data Science

Build statistical foundations required for data analysis.

Descriptive statistics
Probability concepts
Data distributions
Correlation and variability

Practical Tasks

Outcome: Apply statistical concepts to analyze and interpret data.

Module 6: Exploratory Data Analysis (EDA)

Analyze datasets to uncover patterns and relationships.

Data exploration techniques
Handling missing and outlier data
Feature understanding
Insight generation

Practical Tasks
Outcome: Perform exploratory analysis to extract meaningful insights.

Module 7: Introduction to Machine Learning

Understand the basics of machine learning and predictive modeling.

What is machine learning?
Supervised vs unsupervised learning
Common algorithms overview
Model training concepts

Practical Tasks
Outcome: Understand how machine learning models are built and used.

Module 8: Machine Learning with Python

Implement basic machine learning models using Python.

Regression and classification basics
Model evaluation concepts
Overfitting and underfitting
Introduction to model tuning

Practical Tasks
Outcome: Build basic machine learning models using Python.

Module 9: Data Science Tools & Workflows

Learn tools and workflows used by data professionals.

Jupyter Notebook usage
Data science project workflow
Version control basics
Documentation and reporting

Practical Tasks
Outcome: Follow structured workflows for data science projects.

Module 10: Data Science Project & Presentation

Apply all skills to solve a real-world data problem.

Problem definition and data collection
Data analysis and modeling
Insight presentation
Project documentation

Practical Tasks
Outcome: Build and present an end-to-end data science project.

Mr. Mukesh Pandey

Data Science & Analytics Educator | Data Analysis & Visualization

Data science and analytics educator with strong experience in teaching and applying data-driven methodologies. Specializes in data analysis, statistics, data visualization, and exploratory data analysis using industry-relevant tools. Known for simplifying complex data concepts into clear, structured, and practical learning experiences, he helps students understand how to analyze data, identify patterns, and derive meaningful insights. His teaching approach emphasizes hands-on practice, analytical thinking, and real-world applications, enabling learners to build confidence and readiness for data-focused roles.

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