Learn How To:

• Read raw data from different files and data sets.
• Manipulate data using Python programming.
• Use data structures, function, arrays, vectors, data frames in Python.
• Build models using statistics and Python programming.
• Visualize data using Table.
• Create listing, summary, HTML, and graph reports.

Course Outline:

• What is Data Science ?.
• What's the need & why it's in demand ?.
• What is Data Analytics ?.
• Components of Data Science.
• Real-life examples & applications.
• Introduction to different programming languages used for Data Science.

• Why Python for Data Science?.
• Installing Python.
• Python IDEs.
• Python Basic Syntax & Data types.
• Lists.
• String Manipulation.
• Conditional Statements.
• Looping Statements.
• Dictionaries.
• Tuples.
• Functions.
• Array.

• Pandas.
• Numpy.
• Sci-kit Learn.
• Matplot library.
• Seaborn.

• Extracting data from different sources.
• Reading XLSX, CSV etc. files.
• Handling Missing Values.
• Handling Outliers.
• Different Data Munging Techniques.
• One Hot Encoder & Feature Scaling.

• Mean Mode, Median.
• Random Variable.
• Probability, Probability Distribution of Random Variables.
• Type of Random Variables - Based on Scale of Measurement.
• Variance & Standard Deviation.
• Normal, Binomial, Poisson Distribution.
• Standard Normal Distribution and Z-Score.
• Sampling & Sampling Distribution.
• Central Limit Theorem.
• Simulation.
• Hypothesis and hypothesis Testing.
• Hypothesis Testing using z-test, t-test.

• What is Machine Learning ?.
• Overview & Terminologies.
• Difference between AI & ML.
• Supervised & Unsupervised ML.
• Supervised ML Models.
• Linear & Logistic Regression.
• Regression methods, Classification.
• Sampling & Sampling Distribution.
• K Nearest Neighbours KNN.
• Decision Tree, Random Forest.
• Unsupervised ML Models.
• K Means Clustering.
• Under fitting and Overfitting.
• Confusion Metrix.
• K-Fold Cross Validation.
• Regression Evaluation Metrics.
• Time Series Analysis.
• Support Vector Machine (SVM).
• Na'ive Bayes.

• Introduction to Tableau.
• Installation, Tableau and R connectivity.
• Calculated fields, hierarchy, parameters, sets, groups in Tableau.
• Various visualizations Techniques in Tableau.
• Map based visualization using Tableau.
• Adding Totals, sub totals, Captions.
• Formatting Options.
• Using Combined Field, filters.
• Table Calculations.
• Story telling, creating dashboards.

• Project 1.
• Project 2.
• Project 3.
• Project 4.