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Data Science

₹8,999.00

  • 6-Week Intensive Curriculum
  • Accredited by Wipro
  • Internship Opportunities with 100+ partner companies
  • One-on-One Mentorship with industry leaders
  • Letter of Recommendation 
  • Guaranteed Internship Placement
  • Detailed Project Reports for your capstone projects
Category:

Master the Art of Data Science (6-Week Intensive)

Course Duration: 6 Weeks

Mentorship: Industry experts from top tech companies will guide you throughout the course.

Mode of Learning: Online, Self-paced + Weekly Live Sessions

Week 1: Introduction to Data Science & Python

  • Introduction to Data Science: Overview of data science concepts, applications, and the data science lifecycle. Discussion of different roles in data science.
  • Python for Data Science: Setting up the Python environment, data types, variables, control flow, functions, and introduction to key libraries: NumPy and Pandas.
  • Data Wrangling Basics: Reading data from various sources (CSV, JSON, databases), basic data cleaning, and handling missing values.
  • Project 1: Basic Data Exploration – Perform exploratory data analysis (EDA) on a simple dataset using Pandas and Python.

Outcome: Foundational knowledge of data science and Python, ability to perform basic data manipulation and exploration.

Week 2: Data Visualization & Statistical Analysis

  • Data Visualization: Creating effective visualizations using libraries like Matplotlib and Seaborn to understand data patterns and trends.
  • Statistical Analysis: Descriptive statistics, hypothesis testing, probability distributions, and basic statistical modeling.
  • Introduction to SQL: Basic SQL queries for data retrieval and manipulation.
  • Project 2: Data Visualization and Storytelling – Create visualizations to tell a story about a dataset and present findings.

Outcome: Proficiency in data visualization and statistical analysis techniques.

Week 3: Machine Learning Fundamentals

  • Supervised Learning: Introduction to supervised learning algorithms, including linear regression, logistic regression, and decision trees.
  • Model Evaluation: Metrics for evaluating machine learning models (accuracy, precision, recall, F1-score, ROC curves).
  • Feature Engineering: Techniques for selecting, transforming, and creating relevant features for machine learning models.
  • Project 3: Building a Predictive Model – Train and evaluate a predictive model using a supervised learning algorithm.

Outcome: Understanding of fundamental machine learning concepts and algorithms.

Week 4: Advanced Machine Learning Techniques

  • Model Selection and Tuning: Techniques for selecting the best model and optimizing hyperparameters (Grid Search, Random Search, Cross-Validation).
  • Ensemble Methods: Introduction to ensemble methods like Random Forest and Gradient Boosting.
  • Unsupervised Learning: Introduction to clustering (k-means) and dimensionality reduction (PCA).
  • Project 4: Model Optimization and Ensemble Methods – Improve the performance of a machine learning model using tuning and ensemble techniques.

Outcome: Knowledge of advanced machine learning techniques and model optimization strategies

Week 5: Big Data & Cloud Computing

  • Introduction to Big Data: Concepts of big data, challenges, and tools for handling large datasets.
  • Cloud Computing for Data Science: Introduction to cloud platforms (AWS, Azure, GCP) and their services for data storage and processing.
  • Working with Spark (Optional): Basic introduction to Apache Spark for distributed data processing.
  • Project 5: Working with Large Datasets – Process and analyze a large dataset using appropriate tools and techniques. This could involve using cloud-based services.

Outcome: Understanding of big data concepts and cloud computing for data science.

Week 6: Final Project & Career Prep

  • Project 6: Final Data Science Project – Develop a complete data science project, from data collection and cleaning to model building, evaluation, and presentation. Students can choose a project based on their interests.
  • Career Preparation:
    • Portfolio Building: Refine and complete your portfolio with a professional presentation of your projects.
    • Interview Prep & Resume Tips: Learn how to showcase your skills, work on technical interview questions, and perfect your resume.
    • Industry Insights: Get advice on the latest industry trends, frameworks, and technologies.

Outcome: By the end of the course, you’ll have a strong portfolio and the confidence to apply for jobs in data science.

Major Projects:

  • Basic Data Exploration (Week 1) – Core Skills: Python, Pandas, data cleaning.
  • Data Visualization and Storytelling (Week 2) – Core Skills: Data visualization, statistical analysis.
  • Building a Predictive Model (Week 3) – Core Skills: Supervised learning, model evaluation.
  • Model Optimization and Ensemble Methods (Week 4) – Core Skills: Model tuning, ensemble methods.
  • Working with Large Datasets (Week 5) – Core Skills: Big data, cloud computing.
  • Final Data Science Project (Week 6) – Core Skills: Full data science lifecycle, project planning, and execution.

Mentorship at Eduveda Academy:

  • Industry Mentor Assignment: You’ll be paired with a mentor from a top tech company who will provide personalized guidance on projects, career advice, and best practices.
  • 1-on-1 sessions for project reviews and troubleshooting.
  • Live Q&A Sessions weekly with mentors and instructors.

Final Notes:

  • Weekly Live Sessions: These will cover key topics, provide updates, and allow you to ask questions.
  • Peer Networking: Join the academy Slack group for collaboration and feedback.

This 6-week course is intense but designed to give you all the key skills to jumpstart your career in Data Science. You’ll leave with solid project experience, a polished portfolio, and the confidence to apply for jobs in the field.

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