Mastering Data Science
Embark on a comprehensive 330-hour journey to become a proficient Data Scientist. This syllabus covers foundational programming, statistical analysis, machine learning, deep learning, and essential big data technologies, culminating in practical projects and career readiness.
Foundations: Python, Statistics & EDA
Python for Data Science (40 hours)
Build a solid foundation in Python for analytics and data manipulation. Topics include syntax, data types, control flow, functions, data structures, file handling, modules, NumPy, Pandas, and basic visualization with Matplotlib & Seaborn.
Statistics & Probability (30 hours)
Master statistical techniques used in data analysis. Covers descriptive statistics, probability concepts, Bayes Theorem, hypothesis testing (t-test, z-test, chi-square), correlation, ANOVA, sampling, and the Central Limit Theorem.
Exploratory Data Analysis (18 hours)
Understand how to extract insights from raw data, including data cleaning, univariate & bivariate analysis, feature engineering, and handling categorical data. Concludes with an EDA project on a real dataset.
Machine Learning Core Concepts
ML Workflow & Evaluation
Learn the complete machine learning workflow, including train-test split, cross-validation, and key evaluation metrics to assess model performance.
Regression & Classification
Dive into core supervised learning algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forest, KNN, Naive Bayes, and SVM.
Unsupervised Learning
Explore unsupervised techniques such as K-Means and Hierarchical Clustering for pattern discovery in unlabeled data.
Optimization & Tuning
Understand feature scaling, encoding, and hyperparameter tuning methods like GridSearch and RandomizedSearch for optimal model performance.
Deep Learning: Neural Networks
Explore the world of neural networks, from foundational concepts to advanced architectures. This 40-hour module covers:
  • Introduction to Neural Networks, Perceptrons, Activation Functions, and Backpropagation.
  • ANN: Dense Networks using Keras/TensorFlow for various applications.
  • CNN: Convolutional Neural Networks for image data, including Conv2D, Pooling, and Filters.
  • RNN: Recurrent Neural Networks for sequential data, focusing on LSTM and GRU units.
  • Strategies to combat overfitting, such as Dropout and Regularization.
  • Optional Model Deployment using Flask/Streamlit, culminating in deep learning projects for image and sequence data.
Specialized Data Science Domains
Natural Language Processing (20 hours)
Extract insights from text data. Topics include text preprocessing (tokenization, lemmatization, stopwords), Bag of Words, TF-IDF, Sentiment Analysis, Text Classification, and Word Embeddings (Word2Vec, GloVe).
Time Series Forecasting (15 hours)
Analyze and predict sequential data. Covers time series components, lag features, rolling statistics, and models like AR, MA, ARIMA, and SARIMA, concluding with a forecasting project.
SQL for Data Analysis (20 hours)
Learn SQL for querying and manipulating databases. Includes basics (SELECT, WHERE, ORDER BY, JOINs), Group BY, HAVING, Subqueries, Window Functions, CTEs, Views, and Stored Procedures.
Business Intelligence with Power BI
This 20-hour module focuses on creating impactful business dashboards and extracting actionable insights using Power BI.
  • Navigate the Power BI Interface and efficiently load data from various sources.
  • Master Data Modeling and establish effective relationships between datasets.
  • Create diverse visualizations including Bar, Line, Map, Matrix, and KPI charts.
  • Utilize Slicers, Filters, Drill Through, and Bookmarks for interactive reporting.
  • Learn the basics of DAX (Data Analysis Expressions) for advanced calculations.
  • Conclude with a comprehensive Power BI Project, often integrated with SQL for a full data pipeline experience.
Big Data & Cloud Warehousing
PySpark (18 hours)
Master big data analytics using Apache Spark. Covers Spark Architecture, RDD vs DataFrame, DataFrame Operations, Spark SQL, and MLlib Basics, culminating in a PySpark Project.
ETL Process (12 hours)
Understand the ETL pipeline and data flow. Topics include ETL tools & concepts, data extraction (APIs, Files), data transformation techniques, and data loading into databases, with a mini ETL pipeline build.
Snowflake (12 hours)
Utilize a cloud-native data warehouse for analytics. Covers Snowflake Architecture, Warehouses, Databases, Schemas, Tables, data loading & querying, SnowSQL, Python Connector, and Role-Based Access Control.
AWS for Data Science: Key Services
This 8-hour module introduces 15 essential AWS services for data science, covering everything from secure access management (IAM) and virtual servers (EC2) to scalable storage (S3) and managed databases (RDS). Learn about serverless ETL with Glue, querying S3 data with Athena, and running serverless functions with Lambda. Explore SageMaker for end-to-end ML, QuickSight for BI, and API Gateway for model connectivity. Monitor resources with CloudWatch, secure networks with VPC, and automate infrastructure with CloudFormation. Manage Docker images with ECR and orchestrate workflows with Step Functions.
Containerization & Big Data Ecosystems
Docker & Kubernetes (8 hours)
Understand Docker for packaging applications into consistent containers, ensuring portability and easy deployment of ML/Data Science applications. Learn about Dockerfile, CLI, images, containers, Docker Compose, and volume management.
Explore Kubernetes as an orchestration tool for deploying, managing, and scaling containerized applications automatically. Covers Kubernetes Architecture, Pods, Services, and Deployments, highlighting benefits like auto-scaling and self-healing.
Hadoop Ecosystem (6 hours)
Grasp the fundamentals of Hadoop and its ecosystem for big data processing. Topics include characteristics of Big Data, Hadoop History, HDFS (Hadoop Distributed File System) Basics, and the MapReduce programming model.
Learn about YARN for resource management, HDFS Commands for file handling, and Hive for data querying using HiveQL. Concludes with a project to load and process a dataset using Hive or MapReduce.
Employability Skills & Career Readiness
1
Resume Building
Craft ATS-friendly and technical resumes tailored for Data Analyst, Data Engineer, and Data Scientist roles, avoiding common mistakes.
2
LinkedIn Profile Optimization
Build a strong LinkedIn profile, highlight experience, projects, and skills, and learn networking tips to connect with recruiters.
3
GitHub Portfolio
Maintain a clean, organized GitHub profile, master Git version control basics, and write effective README.md files for well-documented projects.
4
Mock Interviews
Practice technical mock interviews (Python, SQL, ML) and prepare for HR interviews using the STAR method, gaining peer and mentor feedback.
5
Professional Communication
Write polished, goal-specific professional emails for job applications, follow-ups, and referrals, and learn the basics of cover letter writing.
Made with