Data Science Course Content

Module 1: Introduction to Data Science

• What is Data Science?
• Why Python for data science?
• Relevance in industry and need of the hour
• How leading companies are harnessing the power of Data Science with
  Python?
• Different phases of a typical Analytics/Data Science projects and role of
  python
• Anaconda vs. Python

Module 2: Python Essentials (Core)

• Overview of Python- Starting with Python
• Introduction to installation of Python
• Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…
• Understand Jupyter notebook & Customize Settings
• Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
• Installing & loading Packages & Name Spaces
• Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
• List and Dictionary Comprehensions
• Variable & Value Labels – Date & Time Values
• Basic Operations – Mathematical – string – date
• Reading and writing data
• Simple plotting
• Control flow & conditional statements
• Debugging & Code profiling
• How to create class and modules and how to call them?

Module 3: Scientific Distributions used in Python for Data Science

• Numpy
• Scipy
• Pandas
• Scikitlearn
• Statmodels
• Nltk……. etc

Module 4: Accessing / Importing and Exporting Data using Python Modules

• Importing Data from various sources (Csv, txt, excel, access etc)
• Database Input (Connecting to database)
• Viewing Data objects – subsetting, methods
• Exporting Data to various formats
• Important python modules: Pandas, beautifulsoup

Module 5: Data Manipulation – Cleansing – Munging using Python Modules

• Cleansing Data with Python
• Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
• Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
• Python Built-in Functions (Text, numeric, date, utility functions)
• Python User Defined Functions
• Stripping out extraneous information
• Normalizing data
• Formatting data
• Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

Module 6: Data Analysis – Visualization using Python

• Introduction exploratory data analysis
• Descriptive statistics, Frequency Tables and summarization
• Univariate Analysis (Distribution of data & Graphical Analysis)
• Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
• Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
• Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc)

Module 7: Basic Statistics & Implementation of Stats Methods in Python

• Basic Statistics – Measures of Central Tendencies and Variance
• Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
• Inferential Statistics -Sampling – Concept of Hypothesis Testing
• Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square
• Important modules for statistical methods: Numpy, Scipy, Pandas

Module 8: Python: Machine Learning – Predictive Modelling – Basics

• Introduction to Machine Learning & Predictive Modeling
• Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
• Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
• Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
• Overfitting (Bias-Variance Tradeoff) & Performance Metrics
• Feature engineering & dimension reduction
• Concept of optimization & cost function
• Concept of the gradient descent algorithm
• Concept of Cross-validation(Bootstrapping, K-Fold validation etc)
• Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

Module 9: Machine Learning Algorithms & Applications – Implementation in Python

• Linear & Logistic Regression
• Segmentation – Cluster Analysis (K-Means)
• Decision Trees (CART/CD 5.0)
• Ensemble Learning (Random Forest, Bagging & boosting)
• Artificial Neural Networks(ANN)
• Support Vector Machines(SVM)
• Other Techniques (KNN, Naïve Bayes, PCA)
• Introduction to Text Mining using NLTK
• Introduction to Time Series Forecasting (Decomposition & ARIMA
• Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
• Fine-tuning the models using Hyperparameters, grid search, piping etc.