Introduction to Data Science/ AI

• Machine Learning Algorithm:
• Sentiment analysis with Machine learning C 5.0
• Support vector Machines
• K Means
• Random Forest
• Naïve Bayes algorithm
• Statistics:
• Correlation
• Linear Regression
• Non Linear Regression
• Predictive time series forecasting
• K means clustering
• P value
• Find outlier
• Neural Network
• Error Measure

Table of Contents

• Basics of Python for Data Analysis
• Why learn Python for data analysis?
• Python 2.7 v/s 3.4 How to install Python?
• Running a few simple programs in Python
• Python libraries and data structures
• Python Data Structures Lists Strings Tuples
• Python Iteration and Conditional Constructs
• Python Libraries
• NumPy
• SciPy
• Matplotlib
• Pandas
• Scikit Learn
• Statsmodels
• Seaborn Bokeh
• Blaze Scrapy
• SymPy
• Requests
• Exploratory analysis in Python using Pandas
• Introduction to series and dataframes
• Data Munging in Python using Pandas
• Building a Predictive Model in Python
• Logistic Regression
• Decision Tree
• Random Forest
• Practice data set –
• Loan Prediction Problem
• Distribution analysis
• Quick Data Exploration
• Importing libraries and the data set:
• Building a Predictive Model in Python
• Logistic Regression Decision
• Tree Random Forest

1. *Introduction to Numpy*: Explain what Numpy is, its uses, and why it’s important in data science and machine learning.

2. *Numpy Arrays*: Discuss the concept of Numpy arrays, including creation, indexing, slicing, and reshaping.

3. *Array Operations*: Cover basic array operations like addition, subtraction, multiplication, and division.

4. *Universal Functions (ufuncs)*: Introduce universal functions and how they can be used to perform element-wise operations on arrays.

5. *Broadcasting*: Explain the concept of broadcasting and how it allows Numpy to work with arrays of different shapes.

Pandas:

1. *Introduction to Pandas*: Explain what Pandas is, its uses, and its role in data manipulation and analysis.

2. *Data Structures*: Discuss the two main Pandas … 

1. *Introduction to Deep Learning*: Explain what deep learning is, its relation to machine learning and artificial intelligence, and its applications.

2. *Neural Networks*: Discuss the structure and function of artificial neural networks, including neurons, layers, and activation functions.

3. *Backpropagation*: Teach students about the backpropagation algorithm, which is used to train neural networks.

Keras:

1. *Introduction to Keras*: Explain what Keras is, its role in deep learning, and its advantages.

2. *Building a Neural Network in Keras*: Show students how to build a simple neural network in Keras, including defining the model architecture, compiling the model, and fitting the model to data.

3. *Training and Evaluating a Model*: Teach students how to train and evaluate a model in Keras, including using callbacks and visualizing training progress.

TABLEAU  or Power BI (Select one ReportingTool)  

1 Tableue Introduction

• Tableue Architecture
• The Tableue Interface
• Distributing and Publishing

2 Tableue Pre Builder

•  The Input Step
•  The Cleaning Step
• Group and Replace
•  The Profile Pane
•  The Pivot Step
• The Aggregate Step
•  The Join Step
• The Union Step

3 Connecting to Data

• Getting Started with Data
• Managing Metadata
• Saving and Publishing Data Sources
• Data Prep with Text and Excel Files
• Join Types with Union
• Cross-database Joins
• Data Blending
• Connecting to PDFs

4 Visual Analytics

• Getting Started with Visual Analytics
• Drill Down and Hierarchies
•  Sorting
•  Grouping
• Creating Sets
• Set Actions
• Ways to Filter
• Using the Filter Shelf
• Interactive Filters
• Parameters
• Formatting
• Basic Tooltips & Viz in Tooltip
• Trend Lines
• Reference Lines
• Forecasting
• Clustering

5 Dashboards and Stories

• Getting Started with Dashboards and Stories
• Building a Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Dashboard Extensions
• Story Point

6 Mapping

• Getting Started with Mapping
• Maps in Tableau
• Editing Unrecognized Locations
• Spatial Files
• The Density Mark Type (Heat maps)
• Expanding Tableau’s Mapping Capabilities
• Custom Geocoding
• Polygon Maps
• Mapbox Integration

7 Calculations

• Getting Started with Calculations
• Calculation Syntax
• Introduction to LOD Expressions
• Intro to Table Calculations
• Modifying Table Calculations
• Aggregate Calculations
• Date Calculations
• Logic Calculations
• String Calculations
• Number Calculations
• Type Calculations
• Conceptual Topics with LOD Expressions
• Aggregation and Replication with LOD Expressions
• Nested LOD Expressions
• How to Integrate R and Tableau
• Using R within Tableau

8 Why Tableue is doing it

• Understanding Pill Types
• Measure Names and Measure Values
• Aggregation, Granularity, and Ratio Calculations
• When to Blend and When to Join
• One-to-many relationships
• Joins inflating the number of rows
• Filtering for Top Across Panes

9 How to Use

• Using a Parameter to Change Fields
• Finding the Second Purchase Date with LOD Expressions
• Cleaning Data by Bulk Re-aliasing
• Bollinger Bands • Bump Charts
• Control Charts • Funnel Charts
• Step and Jump Lines
• Pareto Charts
• Waterfall Charts

Power BI is a Microsoft business analytics service. It provides interactive visualizations and business intelligence capabilities with an interface that Microsoft says is simple enough for end users to create reports and dashboards. It is part of the Microsoft Power Platform.

Syllabus of Power BI (Optional)  

Module 1: Introduction to Power BI   

• Get Started with Power BI
• Overview: Power BI concepts
• Sign up for Power BI
• Overview: Power BI data sources
• Connect to a SaaS solution
• Upload a local CSV file
• Connect to Excel data that can be refreshed
• Connect to a sample
• Create a Report with Visualizations
• Explore the Power BI portal

Module 2: Viz and Tiles

• Overview: Visualizations
• Using visualizations
• Create a new report
• Create and arrange visualizations
• Format a visualization
• Create chart visualizations
• Use text, map, and gauge visualizations and save a report
• Use a slicer to filter visualizations
• Sort, copy, and paste visualizations
• Download and use a custom visual from the gallery

Module 3: Reports and Dashboards

• Modify and Print a Report
• Rename and delete report pages
• Add a filter to a page or report
• Set visualization interactions
• Print a report page
• Send a report to PowerPoint
• Create a Dashboard
• Create and manage dashboards
• Pina report tile to a dashboard
• Pin a live report page to a dashboard
• Pin a tile from another dashboard
• Pin an Excel element to a dashboard
• Manage pinned elements in Excel
• Add a tile to a dashboard
• Build a dashboard with Quick Insights
• Set a Featured (default) dashboard
• Ask Questions about Your Data
• Ask a question with Power BI Q&A
• Tweak your dataset for Q&A
• Enable Cortana for Power BI

Module 4: Publishing Workbooks and Workspace

• Share Data with Colleagues and Others
• Publish a report to the web
• Manage published reports
• Share a dashboard
• Create an app workspace and add users
• Use an app workspace
• Publish an app
• Create a QR code to share a tile
• Embed a report in SharePoint Online

Module 5: Other Power BI Components and Table Relationship

• Use Power BI Mobile Apps
• Get Power BI for mobile
• View reports and dashboards in the iPad app
• Use workspaces in the mobile app
• Sharing from Power BI Mobile
• Use Power BI Desktop
• Install and launch Power BI Desktop
• Get data
• Reduce data
• Transform data
• Relate tables
• Get Power BI Desktop data with the Power BI service
• Export a report from Power BI service to Desktop

Module 6: DAX functions

• New Dax functions
• Date and time functions
• Time intelligence functions
• Filter functions
• Information functions
• Logical functions
• Math & trig functions
• Parent and child functions
• Text functions

Text mining(includes word cloud creation of unstructured data) – Project1

Sentimental analysis of unstructured data – Project 2

Machine learning(Simple linear regression) -Project 4

Machine learning(Multiple linear regression) -Project 5

Machine learning(Logistic Regression) -Project 6

Machine learning(Natural Language Processing) -Project 7

MNC Industry experienced professional with 7 years of experience Live projects