- Introduction to Data Science
- Data Science Life cycle & Process
- Asking Right Questions
- Obtaining Data
- Understanding Data
- Building Predictive Models
- Generating Visualizations For Building Data Products
- Introduction to Data (Types of Data and Datasets)
- Data Quality (Measurement and Data Collection Issues)
- Data pre-processing Stages
- Aggregation
- Sampling
- Dimensionality Reduction
- Feature subset selection
- Feature creation
- Algebraic & Probabilistic View of Data
- Introduction to Python
- Data Science Stack
- Python
- Numpy
- Pandas
- Matplotlib
- Relational Algebra & SQL
- Scraping & Data Wrangling
- assessing
- structuring
- cleaning munging of data
- Basic Descriptive & Exploratory Data Analysis
- Introduction to Text Analysis
- Stemming
- Lemmatization
- Bag of Words
- TF-IDF
- Introduction to Prediction and Inference
- Supervised & Unsupervised Algorithms
- Introduction to Scikit Learn
- Bias-Variance
- Trade-off
- Model Evaluation & Performance Metrics
- Accuracy
- Contingency Matrix
- Precision-Recall
- F-1 Score
- Lift
- Introduction to Map-Reduce paradigm
- Python for Data Analysis, 1st Edition, William McKinney
- An Introduction to Statistical Learning with Applications in R, 1st Edition G. James, 0D. Witten, T. Hastie and R. Tibshirani
- Computational and Inferential Thinking: The Foundations of Data Science, 1 st Edition,A. Adhikari and J. DeNero
- Data Mining and Analysis: Fundamental Concepts and Algorithms, 1 st Edition, M. Zaki & W. Meira
- Data Science from Scratch, 1st Edition, Joel Grus
- Doing Data Science, 1 st Edition, Cathy O'Neil and Rachel Schutt
- Introduction to Data Science. A Python Approach to Concepts, Techniques and Applications, 1st Edition, Laura Igual.