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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
  • Different phases of a typical Analytics/Data Science projects and role of
  • 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.

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Python with Data Science