Data Science & Analytics Course Training @ ANJANA INFOTECH

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.

Syllabus

Introduction to Data Science

• 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

TABLEAU

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

• 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

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

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