Contact Us

Data Science Certification Training

This Data Science course provides the skills to extract meaningful insights from data by leveraging tools, algorithms, and programming languages. It covers a broad range of topics, from foundational concepts like statistics and programming to advanced areas like machine learning, data visualization, and big data technologies. Graduates are equipped to handle both structured and unstructured data and contribute to data-driven decision making in various industries.

Register in
Courses

💡 Key Topics Covered

Introduction to Data Science and Data-Driven Thinking

Data Wrangling: Collection, Cleaning, and Transformation

Exploratory Data Analysis and Visualization (with Matplotlib, Seaborn)

Statistical Inference and Probability

Introduction to Machine Learning (Supervised and Unsupervised Learning)

Regression, Classification, Clustering (e.g., K-Means, Decision Trees)

Model Evaluation and Validation

Working with Big Data (Intro to SQL, Pandas, and NumPy)

Ethics in Data Science and Responsible AI

Capstone Project using real-world datasets

🛠️ Course Features

100% practical and
project-based learning

Real-world
capstone projects

Code reviews and
mentor support

Resume and GitHub
portfolio development

Lifetime access to
course materials (if online)

Certification upon
successful completion

📦 Technologies & Tools Covered:

Python

Pandas

NumPy

Scikit-learn

Matplotlib

Seaborn

Jupyter

Git & GitHub

SQL (basic)

Google Colab or local Python environments

📕 Outcomes

By the end of this course, students will be able to

Understand and apply the core concepts of data science.

Work with structured and unstructured data.

Communicate findings through visualizations and reports.

Apply data science skills to real-world problems.

  • Build and evaluate predictive models.

📕 Data Science Course Content Outline

Module 1: Introduction to Data Science
  • What is Data Science?
  • Data Science vs. Data Analytics vs. Machine Learning
  • The Data Science Workflow (CRISP-DM, OSEMN)
  • Real-world Applications of Data Science
  • Python Basics: Variables, Data Types, Functions, Loops, Conditionals
  • Working with Python libraries
  • NumPy for numerical computing
  • Pandas for data manipulation
  • Matplotlib & Seaborn for data visualization
  • Jupyter Notebooks and Google Colab
  • Types and Sources of Data (structured, unstructured)
  • Importing and Exporting Data (CSV, Excel, JSON, APIs)
  • Handling Missing Values and Duplicates
  • Data Transformation and Feature Engineering
  • Encoding Categorical Variables
  • Descriptive Statistics (mean, median, mode, std, etc.)
  • Data Distributions and Skewness
  • Correlation and Covariance
  • Data Visualization Techniques:
  • Histograms, Box Plots, Scatter Plots
  • Heatmaps, Pairplots
  • Basic Probability Concepts
  • Distributions (Normal, Binomial, Poisson)
  • Sampling and Central Limit Theorem
  • Hypothesis Testing (t-tests, chi-square)
  • Confidence Intervals
  • Supervised vs. Unsupervised Learning
  • Model Lifecycle: Train/Test Split, Overfitting, Underfitting
  • Regression Models
  • Linear Regression
  • Multiple Linear Regression
  • Classification Models:
  • Logistic Regression
  • k-Nearest Neighbors (kNN)
  • Decision Trees & Random Forests
  • Clustering
  • k-Means Clustering
  • Hierarchical Clustering
  • Confusion Matrix, Accuracy, Precision, Recall, F1-score
  • ROC Curve and AUC
  • Cross-Validation
  • Hyperparameter Tuning (Grid Search, Random Search)
  • Introduction to SQL (SELECT, JOIN, GROUP BY, etc.)
  • Connecting Python to SQL databases
  • Basics of Big Data and tools like Hadoop & Spark (optional/overview)
  • Git and GitHub for version control
  • Environment setup (virtualenv, pip, conda)
  • Code documentation and reproducibility
  • Introduction to cloud-based tools (AWS, GCP, Azure – overview)
  • Problem Statement Selection
  • Data Collection and Preprocessing
  • Model Building and Evaluation
  • Report Writing and Presentation
  • Deploying the model (optional, with Flask/Streamlit)
  • Languages: Python, SQL
  • Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
  • Platforms: Jupyter Notebook, Google Colab
  • Version Control: Git & GitHub

💼 Land job at your dream company

🪪 Course Certificate

Showcase your certificate as symbol of couse copliation

Official & Verified

Ideal for fast-paced learning with daily live sessions and hands-on projects

Easily Shareable

Suitable for working professionals or students balancing other commitments

Enhance Credebility

Suitable for working professionals or students balancing other commitments

Better Opportunities

Suitable for working professionals or students balancing other commitments

Industry Leading Placement

Whether you're aspiring to become a full-stack developer, looking to enhance your career, or build your own end-to-end applications, this

Total Students
0
Highest Salary
0 LPA
Avarage Salary
0 LPA
Hiring Partner
0 +

Corporate Training Solutions

Empowering Teams. Driving Results.

At Course Approach, we understand that people are your greatest asset. Our Corporate Training Programs are designed to equip your teams with the skills, tools, and mindset needed to excel in today’s competitive business landscape.

Know More

Quick Links

Courses

Contact Us

  • Info@courseapproach.com
  • +91 97049 62913
  • Vittal Rao nagar , Madhapur, Hyderabad - 500081

© 2025 All Rights Reserved By Course Approach

Developed By Waytowebs