Data Science Syllabus
Full Data Science Syllabus 2023
MODULE 1: PYTHON BASICS • Introduction of python • Installation of Python and IDE • Python objects • Python basic data types • Number & Boolean, strings • Arithmetic Operators • Comparison Operators • Assignment Operators • Operator’s precedence and associativity MODULE 2: PYTHON CONTROL STATEMENTS • IF Conditional statement • IF-ELSE • NESTED IF • Python Loops basics • WHILE Statement • FOR statements • BREAK and CONTINUE statements MODULE 3: PYTHON DATA STRUCTURES • Basic data structure in python • String object basics and inbuilt methods • List: Object, methods, comprehensions • Tuple: Object, methods, comprehensions • Sets: Object, methods, comprehensions • Dictionary: Object, methods, comprehensions MODULE 4: PYTHON FUNCTIONS • Functions basics • Function Parameter passing • Iterators • Generator functions • Lambda functions • Map, reduce, filter functions MODULE 5: PYTHON NUMPY PACKAGE • NumPy Introduction • Array – Data Structure • Core Numpy functions • Matrix Operations MODULE 6: PYTHON PANDAS PACKAGE • Pandas functions • Data Frame and Series – Data Structure • Data munging with Pandas • Imputation and outlier analysis
MODULE 1: DATA SCIENCE ESSENTIALS • Introduction to Data Science • Data Science Terminologies • Classifications of Analytics • Data Science Project workflow MODULE 2: DATA ENGINEERING FOUNDATION • Introduction to Data Engineering • Data engineering importance • Ecosystems of data engineering tools • Core concepts of data engineering MODULE 3: PYTHON FOR DATA SCIENCE • Introduction to Python • Python Data Types, Operators • Flow Control statements, Functions • Structured vs Unstructured Data • Python Numpy package introduction • Array Data Structures in Numpy • Array operations and methods • Python Pandas package introduction • Data Structures : Series and DataFrame • Pandas DataFrame key methods MODULE 4: VISUALIZATION WITH PYTHON • Visualization Packages (Matplotlib) • Components Of A Plot, Sub-Plots • Basic Plots: Line, Bar, Pie, Scatter • Advanced Python Data Visualizations MODULE 5: R LANGUAGE ESSENTIALS • R Installation and Setup • R STUDIO – R Development Env • R language basics and data structures • R data structures , control statements MODULE 6: STATISTICS • Descriptive And Inferential statistics • Types Of Data, Sampling types • Measures of Central Tendencies • Data Variability: Standard Deviation • Z-Score, Outliers, Normal Distribution • Central Limit Theorem • Histogram, Normality Tests • Skewness & Kurtosis • Understanding Hypothesis Testing • P-Value Method, Types Of Errors • T Distribution, One Sample T-Test • Independent And Relational T Tests • Direct And Indirect Correlation • Regression Theory MODULE 7: MACHINE LEARNING INTRODUCTION • Machine Learning Introduction • ML core concepts • Unsupervised and Supervised Learning • Clustering with K-Means • Regression and Classification Models. • Regression Algorithm: Linear Regression • ML Model Evaluation • Classification Algorithm: Logistic Regression
MODULE 1: MACHINE LEARNING INTRODUCTION • What Is ML? ML Vs AI • ML Workflow, Popular ML Algorithms • Clustering, Classification, And Regression • Supervised Vs Unsupervised MODULE 2: ML ALGO: LINEAR REGRESSION • Introduction to Linear Regression • How it works: Regression and Best Fit Line • Modeling and Evaluation in Python MODULE 3: ML ALGO: LOGISTIC REGRESSION • Introduction to Logistic Regression • How it works: Classification & Sigmoid Curve • Modeling and Evaluation in Python MODULE 4: ML ALGO: KNN • Introduction to KNN • How It Works: Nearest Neighbor Concept • Modeling and Evaluation in Python MODULE 5: ML ALGO: K MEANS CLUSTERING • Understanding Clustering (Unsupervised) • K Means Algorithm • How it works : K Means theory • Modeling in Python MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA) • Building Blocks Of PCA • How it works: Finding Principal Components • Modeling PCA in Python MODULE 7: ML ALGO: DECISION TREE • Random Forest Ensemble technique • How it works: Bagging Theory • Modeling and Evaluation in Python MODULE 8: ML ALGO: NAÏVE BAYES • Introduction to Naive Bayes • How it works: Bayes' Theorem • Naive Bayes For Text Classification • Modeling and Evaluation in Python MODULE 9: GRADIENT BOOSTING, XGBOOST • Introduction to Boosting and XGBoost • How it works: weak learners' concept • Modeling and Evaluation of in Python MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE (SVM) • Introduction to SVM • How It Works: SVM Concept, Kernel Trick • Modeling and Evaluation of SVM in Python MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN) • Introduction to ANN • How It Works: Back prop, Gradient Descent • Modeling and Evaluation of ANN in Python MODULE 12: ADVANCED ML CONCEPTS • Adv Metrics (Roc_Auc, R2, Precision, Recall) • K-Fold Cross-validation • Grid And Randomized Search CV In Sklearn • Imbalanced Data Set: Smote Technique • Feature Selection Techniques
MODULE 1: TIME SERIES FORECASTING - ARIMA • What is Time Series? • Trend, Seasonality, cyclical and random • Autoregressive Model (AR) • Moving Average Model (MA) • Stationarity of Time Series • ARIMA Model • Autocorrelation and AIC MODULE 2: FEATURE ENGINEERING • Introduction to Features Engineering • Transforming Predictors • Feature Selection methods • Backward elimination technique • Feature importance from ML modeling MODULE 3: SENTIMENT ANALYSIS • Introduction to Sentiment Analysis • Python packages: TextBlob, NLTK • Case study: Twitter Live Sentiment Analysis MODULE 4: REGULAR EXPRESSIONS WITH PYTHON • Regex Introduction • Regex codes • Text extraction with Python Regex MODULE 5: ML MODEL DEPLOYMENT WITH FLASK • Introduction to Flask • URL and App routing • Flask application – ML Model Deployment MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL • MS Excel core Functions • Pivot Table • Advanced Functions (VLOOKUP, INDIRECT..) • Linear Regression with EXCEL • Goal Seek Analysis • Data Table • Solving Data Equation with EXCEL • Monte Carlo Simulation with MS EXCEL MODULE 7: AWS CLOUD FOR DATA SCIENCE • Introduction of cloud • Difference between GCC, Azure, AWS • AWS Service ( EC2 and S3 service) • AWS Service (AMI), AWS Service (RDS) • AWS Service (IAM), AWS (Athena service) • AWS (EMR), AWS, AWS (Redshift) • ML Modeling with AWS Sage Maker MODULE 8: AZURE FOR DATA SCIENCE • Introduction to AZURE ML studio • Data Pipeline and ML modeling with Azure
MODULE 1: GIT INTRODUCTION • Purpose of Version Control • Popular Version control tools • Git Distribution Version Control • Terminologies • Git Workflow • Git Architecture MODULE 2: GIT REPOSITORY and GitHub • Git Repo Introduction • Create New Repo with Init command • Copying existing repo • Git user and remote node • Git Status and rebase • Review Repo History • GitHub Cloud Remote Repo MODULE 3: COMMITS, PULL, FETCH AND PUSH • Code commits • Pull, Fetch and conflicts resolution • Pushing to Remote Repo MODULE 4: TAGGING, BRANCHING, AND MERGING • Organize code with branches • Checkout branch • Merge branches MODULE 5: UNDOING CHANGES • Editing Commits • Commit command Amend flag • Git reset and revert MODULE 6: GIT WITH GITHUB AND BITBUCKET • Creating GitHub Account • Local and Remote Repo • Collaborating with other developers • Bitbucket Git account
MODULE 1: BIG DATA INTRODUCTION • Big Data Overview • Five Vs of Big Data • What is Big Data and Hadoop • Introduction to Hadoop • Components of Hadoop Ecosystem • Big Data Analytics Introduction MODULE 2: HDFS AND MAP REDUCE • HDFS – Big Data Storage • Distributed Processing with Map Reduce • Mapping and reducing stages concepts • Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort • Hands-on Map Reduce task MODULE 3: PYSPARK FOUNDATION • PySpark Introduction • Spark Configuration • Resilient distributed datasets (RDD) • Working with RDDs in PySpark • Aggregating Data with Pair RDDs MODULE 4: SPARK SQL and HADOOP HIVE • Introducing Spark SQL • Spark SQL vs Hadoop Hive • Working with Spark SQL Query Language MODULE 5: MACHINE LEARNING WITH SPARK ML • Introduction to MLlib Various ML algorithms supported by MLib • ML model with Spark ML. • Linear regression • logistic regression • Random forest MODULE 6: KAFKA and Spark • Kafka architecture • Kafka workflow • Configuring Kafka cluster • Operations
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION • What Is Business Intelligence (BI)? • What Bi Is The Core Of Business Decisions? • BI Evolution • Business Intelligence Vs Business Analytics • Data Driven Decisions With Bi Tools • The Crisp-Dm Methodology MODULE 2: BI WITH TABLEAU: INTRODUCTION • The Tableau Interface • Tableau Workbook, Sheets And Dashboards • Filter Shelf, Rows And Columns • Dimensions And Measures • Distributing And Publishing MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE • Connecting To Data File , Database Servers • Managing Fields • Managing Extracts • Saving And Publishing Data Sources • Data Prep With Text And Excel Files • Join Types With Union • Cross-Database Joins • Data Blending • Connecting To Pdfs MODULE 4: TABLEAU: BUSINESS INSIGHTS • Getting Started With Visual Analytics • Drill Down And Hierarchies • Sorting & Grouping • Creating And Working Sets • Using The Filter Shelf • Interactive Filters • Parameters • The Formatting Pane • Trend Lines & Reference Lines • Forecasting • Clustering MODULE 5: DASHBOARDS, STORIES AND PAGES • Dashboards And Stories Introduction • Building A Dashboard • Dashboard Objects • Dashboard Formatting • Dashboard Interactivity Using Actions • Story Points • Animation With Pages MODULE 6: BI WITH POWER-BI • Power BI basics • Basics Visualizations • Business Insights with Power BI
MODULE 1: DATABASE INTRODUCTION • DATABASE Overview • Key concepts of database management • CRUD Operations • Relational Database Management System • RDBMS vs No-SQL (Document DB) MODULE 2: SQL BASICS • Introduction to Databases • Introduction to SQL • SQL Commands • MY SQL workbench installation • Comments • import and export dataset MODULE 3: DATA TYPES AND CONSTRAINTS • Numeric, Character, date time data type • Primary key, Foreign key, Not null • Unique, Check, default, Auto increment MODULE 4: DATABASES AND TABLES (MySQL) • Create database • Delete database • Show and use databases • Create table, Rename table • Delete table, Delete table records • Create new table from existing data types • Insert into, Update records • Alter table MODULE 5: SQL JOINS • Inner join • Outer join • Left join • Right join • Cross join • Self join MODULE 6: SQL COMMANDS AND CLAUSES • Select, Select distinct • Aliases, Where clause • Relational operators, Logical • Between, Order by, In • Like, Limit, null/not null, group by • Having, Sub queries MODULE 7: DOCUMENT DB/NO-SQL DB • Introduction of Document DB • Document DB vs SQL DB • Popular Document DBs • MongoDB basics • Data format and Key methods • MongoDB data management
MODULE 1: ARTIFICIAL INTELLIGENCE OVERVIEW • Evolution Of Human Intelligence • What Is Artificial Intelligence? • History Of Artificial Intelligence. • Why Artificial Intelligence Now? • Ai Terminologies • Areas Of Artificial Intelligence • Ai Vs Data Science Vs Machine Learning MODULE 2: DEEP LEARNING INTRODUCTION • Deep Neural Network • Machine Learning vs Deep Learning • Feature Learning in Deep Networks • Applications of Deep Learning Networks MODULE 3: TENSORFLOW FOUNDATION • TensorFlow Installation and setup • TensorFlow Structure and Modules • Hands-On: ML modeling with TensorFlow MODULE 4: COMPUTER VISION INTRODUCTION • Image Basics • Convolution Neural Network (CNN) • Image Classification with CNN • Hands-On: Cat vs Dogs Classification with CNN Network MODULE 5: NATURAL LANGUAGE PROCESSING (NLP) • NLP Introduction • Bag of Words Models • Word Embedding • Language Modeling • Hands-On: BERT Algorithm MODULE 6: AI ETHICAL ISSUES AND CONCERNS • Issues And Concerns Around Ai • Ai And Ethical Concerns • Ai And Bias • Ai: Ethics, Bias, And Trust
