Data Science Using Python

Data Science Using Python course is offered by TCIL-IT. TCIL-IT Chandigarh is a fastest emerging company in the IT and telecommunications industry. Being a well accredited company, we have specialized in the field of various industrial training programs.

Modules:

• Module1: Introduction
• What Data Science?
• Common Terms in Analytics
• Analytics vs. Data warehousing, OLAP, MIS Reporting
• Relevance in industry and need of the hour
• Types of problems and business objectives in various industries
• How leading companies are harnessing the power of analytics?
• Critical success drivers
• Overview of analytics tools & their popularity
• Analytics Methodology & problem solving framework
• List of steps in Analytics projects
• Identify the most appropriate solution design for the given problem statement
• Project plan for Analytics project & key milestones based on effort estimates
• Build Resource plan for analytics project
• Why Python for data science?
• Module2: Core Python
• Overview of Python- Starting with Python
• Introduction to installation of Python
• Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
• Python Syntax
• Variables & Data Types
• Operators
• Conditional Statements
• Working With Numbers & Strings
• Collections API
• LISTS
• TUPLES .
• DICTIONARY   
• Date and Time
• Function & Modules
• File handling
• Exception Handling
• OOPS Concepts in python
• Regular Expression
• Module 3: Python Libraries for Data Science
• Numpy
• SciPy
• Pandas
• Matplotlib
• Seaborn
• scikitlearn
• statmodels
• nltk
• Module 4: Python Modules for Access, Import/Export Data
• 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 , beautiful soup
• Module 5: Data Manipulation, Cleansing and Munging
• 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 and Visualization
• 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.)
• Data visualization with tableau.
• Module 7: Statistics
• 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, Correlations and Chi-square
• Important modules for statistical methods: Numpy, Scipy, Pandas
• Module 8: Predictive Modeling
• Concept of model in analytics and how it is used?
• Common terminology used in analytics & modeling process
• Popular modeling algorithms
• Types of Business problems - Mapping of Techniques
• Different Phases of Predictive Modeling
• Module 9: Data Exploration for Modeling
• Need for structured exploratory data
• EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
• Identify missing data
• Identify outliers data
• Visualize the data trends and patterns
• Module 10: Data Preparation
• Need of Data preparation
• Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
• Variable Reduction Techniques - Factor & PCA Analysis
• Module 11: Solving Segmentation Problems
• Introduction to Segmentation
• Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
• Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
• Behavioral Segmentation Techniques (K-Means Cluster Analysis)
• Cluster evaluation and profiling - Identify cluster characteristics
• Interpretation of results - Implementation on new data
• Module 12: Linear Regression
• Introduction - Applications
• Assumptions of Linear Regression
• Building Linear Regression Model
• Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
• Assess the overall effectiveness of the model
• Validation of Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
• Interpretation of Results - Business Validation - Implementation on new data
• Module 13: Logistic Regression
• Introduction - Applications
• Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
• Building Logistic Regression Model (Binary Logistic Model)
• Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
• Validation of Logistic Regression Models (Re running Vs. Scoring)
• Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
• Interpretation of Results - Business Validation - Implementation on new data
• Module 14: Time Series Forecasting
• Introduction - Applications
• Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
• Classification of Techniques(Pattern based - Pattern less)
• Basic Techniques - Averages, Smoothening, etc
• Advanced Techniques - AR Models, ARIMA, etc
• Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc
• Module 15: Project work
• Applying different algorithms to solve the business problems and bench mark the results