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360 Data Science Expert

75,575.00

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Home > Courses > 360 Data Science Expert

Become a

₹75,575 + taxes
Avail upto 80% scholarship

Learn a host of technologies that help you master the
finer nuances of Sourcing & Storage, Steaming & Integration,
Mining & Cleaning, Analytics & Visualisation, with Skill Sigma.
Over 30k professionals certified.
Home > Courses > 360 Data Science Expert

Become a

₹75,575 + taxes
Avail upto 80% scholarship

Learn a host of technologies that help you master the finer nuances of Sourcing & Storage, Steaming & Integration, Mining & Cleaning, Analytics & Visualisation, with Skill Sigma.
Over 30k professionals certified.

Key Highlights

No programming experience required.
Training on Data Engineering, Analytics, Machine Learning, Neural Nets and Visualization.
Designed for learners from all backgrounds.
Get job-ready in multiple areas of Data Science.
Career assistance programs for all learners.
Mentored more than 2000+ learners on Data Science and Analytics.
Adaptive learning plans with classes available online or in-class.

Cource Includes

The World of Data Science - An Introduction

Data Science in Practice
RDBMS & SQL

  • File Management System
  • Disadvantages of a File Management System in a Multi-user Environment.
  • DBMS concepts, RDBMS concepts.
  • Features of RDBMS
  • Communication Language to RDBMS – SQL
  • SQL Practicals — DDL,DML,DCL and TCL Commands
Data Warehousing Introduction and Evolution
  • Data Warehouse Concepts
  • Characteristics of DWH, Need for a DWH for Business Intelligence
  • Difference between OLTP and OLAP
  • The Architecture of DWH. Asset Assembly to Asset Exploitation
Unix Operating System
  • Operating System Introduction
  • Unix Essentials
  • Unix Commands & Interface
Data Engineering – The World of Big Data
  • What is Big Data?
  • Characteristics of Big Data
  • Challenges of Big Data
  • Main Sources of Big Data
  • Big Data Analytics and Applications of Big Data
  • Traditional Data Architecture and Modern Data Architecture
  • Big Data Use Cases & Industry Examples
Hadoop
  • What is Hadoop?
  • Why Hadoop?
  • Advantages of Hadoop
  • History of Hadoop
  • Hadoop Key characteristics
    – Reliability
    – Scalability
    – Flexibility
    – Economical
    – Robust
  • RDBMS vs Hadoop
  • Hadoop Architecture and Ecosystem
  • When to Use and Not Use Hadoop
Yarn
  • What is Yarn?
  • Advantages of using Yarn
  • Yarn Architecture
  • Applications of yarn
  • What is Map Reduce?
  • Understanding the Limitations of MapReduce in Hadoop
Apache Spark & Scala
  • Introduction to Spark
  • History of Spark
  • Components of a spark programming
  • Advantages of Spark
  • Spark Architecture
  • Spark Use Cases
  • Introduction to Scala
  • What is SBT? (Scala Built Tool)
  • Resilient Distributed Datasets and its Operation
  • RDD Operations – Map,Union, FlatMap, Intersect, Distinct,SortBy,Zip
  • More RDD Operations – Sampling, Statistical, Other Operations
  • Pair RDD Operations –countByKey, groupByKey, sortByKey
  • Transformations supported by spark includes single-RDD and multi-RDD transformations
  • What is Spark SQL?
  • Features of Spark SQL
  • Uses of Spark SQL
  • Data Frames
  • Creating Data Frames
  • Demo
R Programming

Introduction to R

  • Math, Variables, and Strings
  • Vectors and Factors
  • Vector operations

Data structures in R

  • Arrays & Matrices
  • Lists
  • Data frames

R programming fundamentals

  • Conditions and loops
  • Functions in R
  • Objects and Classes
  • Debugging

Working with data in R

  • Reading CSV and Excel Files
  • Reading text files
  • Writing and saving data objects to file in R

Strings and Dates in R

  • String operations in R
  • Regular Expressions
  • Dates in R

Apache Spark & Scala

  • Introduction to Spark
  • History of Spark
  • components of a spark programming
  • Advantages of Spark
  • Spark Architecture
  • Spark Use Cases

Introduction to Scala

  • What is SBT(Scala built tool)
  • Resilient Distributed Datasets and its Operation
  • RDD Operations – Map,Union,FlatMap,intersect,distinct,SortBy,Zip
  • More RDD Operations – Sampling,Statistical,Other Operations
  • Pair RDD Opearations –countByKey,groupByKey,sortByKey,join
  • Transformations supported by spark includes single-RDD and multi-RDD transformations

What is Spark SQL

  • Features of Spark SQL
  • Uses of Spark SQL
  • Data Frames
  • Creating Data Frames
  • Demo

R Programming
Introduction to R

  • Math, Variables, and Strings
  • Vectors and Factors
  • Vector operations

Data structures in R

  • Arrays & Matrices
  • Lists
  • Data frames

R programming fundamentals

  • Conditions and loops
  • Functions in R
  • Objects and Classes
  • Debugging

Working with data in R

  • Reading CSV and Excel Files
  • Reading text files
  • Writing and saving data objects to file in R

Strings and Dates in R

  • String operations in R
  • Regular Expressions
  • Dates in R
Python Programming for Data Science
  • Introduction to Python
  • Python History
  • Python Applications
  • Python Install, Python Path
  • Python Example, Execute Python

Datatypes, Declarations and Comments

  • Python Variables and Data Types
  • Python Keywords
  • Python Literals, Python Comments
  • Sample Programs for the above

Operators in Python

  • Arithmetical Operators
  • Relational Operators
  • Logical Operators
  • Assignment Operators
  • Sample programs for the above

Conditional Statements

  • Simple IF, If and Else, Nested If
  • Sample program using if conditions

Python Loops

  • Python for loop, Python while loop

Python Loops

  • Python Break, Python Continue, Python Pass

Python Data Structures or Collections

  • Lists
  • Tuples
  • Named Tuple
  • Sets (Default set, Frozen Set, Union, Intersect, Minus)
  • Dictionaries
  • Un-ordered Dictionary
  • Ordered Dictionary
  • ChainMap
  • Counter

Python String Handling and Functions

  • Handling string format with f-string
  • capitalize(),center(),count(),endswith(),format(),rjust(), ljust()
  • len(),replace(),upper(),lower(),split()with Examples

Number Functions

  • abs() ,ceil(), floor(), cmp(), exp(), log(), log10()
  • min(), max(), power(), round(), sqrt()With Examples

Date Functions

  • Import Datetime Module
  • Now(),Datetime()
  • Import Calendar
  • Calendar.Month(),Calendar.prcal(2019)
  • Import Time

User-defined Functions in Python

  • Required Argument Function
  • Keyword Argument Function
  • Varying Argument Functions
  • Default Argument Functions
  • Position only Parameter Functions

File Handling in Python

  • Python Files I/O
  • create file using “r”, “w” ,”a” modes
Statistics for Data Science
  • Introduction
  • Basic Statistics
  • Useful Statistics in Analytics & Data Science
  • Central Tendency
  • Normal Distribution
  • Hypothesis Testing
Machine Learning

Machine Learning For Data Science & Analytics
Machine learning vs. Statistical modelling
Supervised vs. Unsupervised Learning

  • Machine Learning Languages, Types, and Examples
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning

Supervised Learning

  • Understanding Nearest Neighbour Classification
  • The KNN algorithm
  • Measuring Similarity with Distance
  • Choosing Appropriate K
  • Use Case

Classification Using Naïve Bayes

  • Basic Concepts of Bayesian Methods
  • Probabilistic Learning

Classification using Decision Trees

  • The C5.0 Decision Tree Algorithm
  • Understanding Classification Rules
  • Separate and Conquer
  • Rules from Decision Trees
  • Advantages & Disadvantages of Decision Trees

Understanding Regression

  • Simple Linear Regression
  • Ordinary Least Square estimation

Correlations
Multiple Linear Regression
Support Vector Machines

  • Classification with Hyper planes
  • Using Kernels for non-linear spaces

Unsupervised Learning
Association Rules – Pattern detection

  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances Between Clusters – Single Linkage Clustering
  • Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
  • Density-Based Clustering

Neural Networks

  • Black Box Methods
  • Training neural networks with backpropagation
  • ANN – Artificial Neural Networks
  • CNN – Convolutional Neural Networks
  • Evaluating Model Performance
  • Improving Model Performance

Statistics for Data Science

  • Introduction
  • Basic Statistics
  • Useful statistics in Analytics & Data Science
  • Central Tendency
  • Normal Distribution
  • Hypothesis Testing

Machine Learning
Machine Learning For Data Science & Analytics
Machine learning vs. Statistical modelling
Supervised vs. Unsupervised Learning

  • Machine Learning Languages, Types, and Examples
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning

Supervised Learning

  • Understanding nearest neighbour classification
  • The KNN algorithm
  • Measuring similarity with distance
  • Choosing Appropriate K
  • Use Case

Classification Using Naïve Bayes

  • Basic Concepts of Bayesian Methods
  • Probabilistic Learning

Classification using Decision Trees

  • The C5.0 decision tree algorithm
  • Understanding Classification Rules
  • Separate and Conquer
  • Rules from decision trees
  • Advantages & Disadvantages of Decision Trees

Understanding Regression

  • Simple Linear Regression
  • Ordinary least Square estimation

Correlations
Multiple Linear Regression

Support Vector Machines

  • Classification with Hyper planes
  • Using Kernels for non-linear spaces

Unsupervised Learning
Association Rules – Pattern detection

  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances Between Clusters – Single Linkage Clustering
  • Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
  • Density-Based Clustering

Neural Networks

  • Black Box Methods
  • Training neural networks with back propagation
  • ANN – Artificial Neural Networks
  • CNN – Convolutional Neural Networks

Evaluating Model Performance
Improving Model Performance

Data Visualization with Tableau

Connecting to Data
Customizing a Data Source

  • Filtering Your Data
  • Sorting Your Data
  • Creating Groups in Your Data
  • Creating Hierarchies in Your Data
  • Working with Date Fields: Discrete and Continuous Time
  • Working with Date Fields: Custom Dates
  • Working with Multiple Measures: Dual Axis and Combo Charts
  • Working with Multiple Measures: Combined Axis Charts
  • Showing Relationships between Numerical Values
  • Mapping Data Geographically
  • Using Crosstabs: Totals and Aggregation

Using Crosstabs: Highlight Tables

  • Using Crosstabs: Heat Maps
  • Using Calculations: Customize Your Data
  • Using Calculations: Working with Strings, Dates, and Type Conversion Functions
  • Using Calculations: Working with Aggregations
  • Using Quick Table Calculations to Analyze Data
  • Showing Breakdowns of the Whole
  • Highlighting Data with Reference Lines
  • Create a Dashboard: Combining Your Views
  • Create a Dashboard: Add Actions for Interactivity
  • Sharing Your Work

Working with a Data Extract

  • Joining Tables
  • Blending Multiple Data Sources
  • Blending Data without a Common Field
  • Using Split and Custom Split
  • Advanced Calculations: Aggregating

Dimensions

  • Controlling Table Calculations
  • Showing the Biggest and Smallest Values
  • Using Level of Detail Expressions
  • Filtering and LOD Expressions
  • Using Parameters to Control Data in the View
  • Parameters: Swap Measures

Using Sets to Highlight Data

  • Advanced Mapping: Modifying Locations
  • Advanced Mapping: Customizing Tableau’s Geocoding
  • Advanced Mapping: Using a Background Image
  • Viewing Distributions
  • Comparing Measures Against a Goal
  • Showing Statistics and Forecasting: Use the Analytics Pane and Trend Lines Advanced

Dashboards: Using Design Techniques and Filter Actions
Telling Stories with Data

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