Certified Data Scientist – Associate

COURSE DESCRIPTION

Data Science works on top of big data for data analysis and driving information for enabling business decisions. There is no value for big data unless its driving meaningful information for business leaders or stakeholders. Data Scientists
are hugely in demand. There is a huge gap between demand for data scientists and supply in the industry. Its best time for you to jump into this domain.

KEY TECHNOLOGIES

Advanced Python, Numpy, Pandas, Matplotlib, Plotly, Machine Learning, Deep learning, Statistical modelling, Advanced Excel, SQL for analysis, Advanced Tableau

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Certified Data Scientist – Associate Fee

Rs. 25,000/- + GST(18%) (Indian)
$499 (International)

You will get a certificate of Certified Data Scientist – Associate from International Society of Data Science after attending all sessions and completing all assignments/quizzes/exams.

COURSE CURRICULUM COVERAGE INFORMATION

DATA SCIENCE OVERVIEW

  • Introduction to Data Science
  • Different Sectors Using Data Science
  • Purpose and Components of Python

DATA ANALYTICS OVERVIEW

  • Data Analytics Process
  • Knowledge Check
  • Exploratory Data Analysis(EDA)
  • EDA-Quantitative Technique & Graphical Technique
  • Data Analytics Conclusion or Predictions
  • Data Analytics Communication
  • Data Types and Plotting

STATISTIAL MODELLING

  • Introduction to Statistics
  • Statistical and Non-statistical Analysis
  • Major Categories of Statistics
  • Statistical Analysis Considerations
  • Population and Sample
  • Statistical Analysis Process
  • Data Distribution
  • Dispersion
  • Histogram
  • Measures of Central Tendency
  • Understanding the spread of data
  • Introduction to Probability
  • Probabilities of discrete and continuous variables
  • Central Limit theorem and the Normal Distribution
  • Introduction to Inferential Statistics
  • Understanding the Confidence Interval and the margin of error
  • Hypothesis Testing
  • T tests
  • Chi Squared tests
  • Understanding the concept of Correlation
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • K-Means

INTRDUCTION TO PYTHON

  • Getting Started
  • Python Environment setup
  • Anaconda installation of python

PROGRAMMING WITH PYTHON

  • Strings
  • Lists
  • Dictionaries
  • Tuples
  • Functions
  • Conditional and Looping Construct

OBJECT ORIENTED PROGRAMMING

  • concept of Object oriented Programming
  • Classes
  • Inheritance

ADVANCE PROGRAMMING IN PYTHON

  • Liner List Manipulation
  • Stacks & Queues in list
  • Data File Handling
  • Exception Handling & Generate Functions

MATHEMATICAL COMPUTING WITH PYTHON (NUMPY)

  • Introduction to Numpy
  • Activity-Sequence it Right
  • Class and Attributes of and array
  • Basic Operations
  • Mathematical Functions of Numpy

DATA MANIPULATION WITH PANDAS

  • Understanding Data Frame
  • View and Select Data Demo
  • Missing Values
  • Data Operations
  • File Read and Write Support
  • Pandas Sql Operation

SCIENTIFIC COMPUTING WITH PYTHON (SCIPY)

  • Introduction & SciPy Sub Package – Integration and Optimization
  • SciPy sub package
  • Demo – Calculate Eigenvalues and Eigenvector
  • SciPy Sub Package – Statistics, Weave and IO

MACHINE LEARNING WITH SCIKIT–LEARN

  • Introduction to Machine Learning
  • Machine Learning Approach
  • How Supervised and Unsupervised Learning Models Work
  • Scikit-Learn
  • Supervised Learning Models – Linear Regression
  • Supervised Learning Models: Logistic Regression
  • K Nearest Neighbors (K-NN) Model
  • Unsupervised Learning Models: Clustering
  • Unsupervised Learning Models: Dimensionality Reduction
  • Pipeline
  • Model Persistence
  • Model Evaluation – Metric Functions

NATURAL LANGUAGE PROCESSING WITH SCIKIT LEARN

  • NLP Overview
  • NLP Approach for Text Data
  • NLP Environment Setup
  • NLP Sentence analysis
  • NLP Applications
  • Major NLP Libraries
  • Scikit-Learn Approach
  • Scikit – Learn Approach Built – in Modules
  • Scikit – Learn Approach Feature Extraction

DATA VISUALIZATION IN PYTHON USING MATPLOTLIB

  • Introduction to Data Visualization
  • Python Libraries
  • Plots
  • Matplotlib Features
  • Types of Plots and Seaborn

TABLEAU

  • Installation and required Environment
  • Connecting Tableau to Different Files, Server Files
  • Navigation in Tableau, Formatting, Adding Labels, Colouring etc.
  • Working with Data (understanding Aggregation, Granularity, LOD)
  • Joining and Blending Data, Understanding Hierarchies and Joints( Inner , Outer , Left or Right)
  • Creating Different charts, Plots, Dual axis charts etc.
  • Understanding Timelines
  • Creating Dashboard, Storytelling
  • Working Geographical Data, creating Custom Territories, Clustering
  • Creating Groups and sets, Adding Filter, Calculated Field, Quick Table Calculation,
    Parameters
  • Analytics in tableau(Box Plot, Moving Average, Trend Lines, Adding Reference Line, etc.)
  • Animation in Tableau

DBMS & SQL For Analysis

  • Overveiw
  • RDBMS Concept
  • Databases & Data Types
  • Creating table , working with tables
  • Queries(Insert , select, update , Delete)
  • Clauses
  • Orderby , Groupby
  • Distinct Keywords
  • Constraints
  • Joints
  • Truncate Table
  • Having Clause
  • Clone Table
  • Using Sequence
  • Handling Duplicate





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