Data Science Mod - Engineer Sahab Education

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Data Science Essentials | Begin Your Career in Data Science

Data Science Bootcamp
Duration 20 Weeks

Embark on a transformative journey into the world of data science with our intensive 5-month bootcamp in Indore. Designed for both beginners and professionals aiming to upskill, our program offers hands-on training in essential tools like Python, SQL, and machine learning techniques. Under the guidance of seasoned industry experts, you'll work on real-world projects, build a strong portfolio, and acquire the skills sought after in today's data-driven industries.

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What you’ll learn in 20 Weeks?

Our comprehensive curriculum is designed to equip you with essential data science skills:

Programming & Data Handling: Master Python and SQL for data manipulation and analysis.

Statistical Analysis & Machine Learning: Gain proficiency in statistical techniques and machine learning algorithms.

Real-World Projects: Work on practical projects that mirror current industry challenges.

Module 1

Python Mastery for Data Science

  • Getting started: IDEs (Jupyter, PyCharm), setup & environments
  • Core Python: Variables, Data Types, Operators, Conditionals, Loops
  • Functional Python: Functions, Lambdas
  • Pythonic Thinking: List comprehensions, error handling, modules
  • Object-Oriented Python: Classes, Inheritance, Encapsulation
Module 2

Data Handling & Visualization with Python

  • NumPy: Arrays, indexing, vectorized operations, math & stats
  • Pandas: Series, DataFrames, cleaning, transformation, group operations
  • Data Merging, Filtering, and Reshaping Techniques
  • Data Visualization: Matplotlib essentials and Seaborn for advanced plots
Module 3

Exploratory Data Analysis & Statistics

  • Univariate, Bivariate, Multivariate Analysis
  • Mean, Median, Mode, Variance, Standard Deviation
  • Correlation, Covariance, and Distribution Patterns
  • Probability Concepts and Statistical Tests
  • Data Health Check: Missing values, outliers, data quality
Module 4

SQL for Data Insights

  • Introduction to Relational Databases and SQL Syntax
  • Data Retrieval with SELECT, WHERE, GROUP BY, ORDER BY
  • Combining Data with JOINs and Subqueries
  • Window Functions and Aggregations
Module 5

Data Visualization with Power BI

  • Power BI Overview & Installation
  • Power Query: Data Transformation
  • Data Modeling & Relationships
  • Writing DAX Functions
  • Creating Visuals and Reports
  • Dashboards & Sharing Online
Module 6

Data Visualization with Tableau

  • Tableau Interface & Navigation
  • Connecting to Various Data Sources
  • Worksheets, Dashboards, Stories
  • Filters, Parameters, Calculated Fields
  • Advanced Visuals & Interactivity
Module 7

Machine Learning Fundamentals & Feature Engineering

  • Machine Learning Workflow and Use Cases
  • Data Splitting: Train/Test, Cross-Validation
  • Feature Engineering: Encoding, Scaling, Transformation
  • Missing Data Imputation and Handling Imbalance
  • Introduction to scikit-learn and Pipelines
Module 8

Supervised & Unsupervised Learning in Action

  • Supervised: Linear/Logistic Regression, KNN, Decision Trees, Random Forest, Naive Bayes
  • Unsupervised: K-Means, Hierarchical Clustering, PCA
  • Evaluation Metrics: Confusion Matrix, F1 Score
Module 9

Foundations of Deep Learning

  • Neural Networks: Architecture, Neurons, Activation Functions
  • Forward & Backpropagation, Optimizers: SGD, Adam
  • Loss Functions, TensorFlow & Keras
  • Model Types: Dense Networks and Intro to CNNs
Module 10

Natural Language Processing with Python

  • Text Preprocessing: Tokenization, Stemming, Lemmatization
  • Feature Extraction: Bag-of-Words, TF-IDF, Word Embeddings
  • Building NLP Models: Text Classification, Sentiment Analysis
  • Intro to Transformers & BERT
Module 11

Generative AI and LLMs

  • Introduction to Generative AI
  • Overview of Large Language Models (LLMs)
  • Working with Pretrained Models
  • Prompt Engineering & LangChain Basics
  • Retrieval-Augmented Generation (RAG)
  • Ethical Considerations in Generative AI

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Frequently asked Questions

Check out some frequently asked questions.

Our course is beginner-friendly and requires no prior design experience. However, basic computer literacy and an interest in design are helpful.

The course covers a comprehensive range of UI/UX design principles, including user research, information architecture, interaction design, visual design, and prototyping.

You'll gain hands-on experience with industry-standard UX/UI design tools like Figma, Adobe XD, Photoshop, Illustrator and Miro.

UI/UX design is a rapidly growing field with diverse career paths such as UX/UI Designer, User Researcher, Interaction Designer, and Visual Designer.

Yes, the course includes career guidance modules to help you build your portfolio, prepare for interviews, and navigate the job market.

Many courses offer alumni communities or mentorship programs for ongoing support after completion.

Course fees vary depending on format and duration. We offer flexible payment options such as installments.

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