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Why enroll in a data science course

India has seen an increase in people taking data science courses. This increase began in 2013 and since then, there has been more awareness of data science and hence the data certification company in lucknow. The increase has prompted most educational institution to provide a course in data science. To fully master the skills associated with data science, there are two categories that you need to learn. There are the skills that you can learn through an online course like the one offered by Digital Navigators but the other you will need to learn in a classroom setting.


What is Data Science

A library framework for software is the perfect definition of data science. The process allows for distributing large amounts of data by means of a programming tool across a multitude of computers. This can be increased very easily from one server to many machines. Data science covers a lot of topics and learning this science allows you to learn many skills including programming, math, machine learning and many others

Why it is an excellent platform

This is an excellent platform and the Data Science Certification Training Course in Lucknow taught by Digital Navigators will open many doors to new opportunities to ensure a successful career. You will be able to make all the right decisions from the insight gained. You need to enroll in our Data Science Training Course in Lucknow to get certified inn machine learning and other subject areas right here in Lucknow.

Online Data Science Course

As expressed before, in class learning of data science is just part of it. You still have the option to enroll in the online course offered by Digital Navigators The great thing about online Data Science Training in Lucknowis that you get to learn from the comfort of your own home. You get to set your time so you will always be in charge of your education. When you decide to enroll in a data science course there are a few questions that you should ask.

Introduction to Data Science

  • What is Data Analytics & Data Science

  • Different types of Data Analytics (Descriptive, Predictive, Prescriptive)
  • What is Artificial Intelligence
  • Machine Learning (Supervised & Unsupervised Learning)
  • Deep Learning (Artificial Neural Networks, CNN)
  • Overview of Banking, Healthcare, Telecom domain
  • Real world Applications of Machine Learning & Deep Learning
  • What to expect from this course (Salary, Market trends, job roles, Domain

Introduction to SQL

Basic SQL

  • What is SQL
  • SQL in Python
  • SQL Architecture
  • SQL Syntax
  • Data Types
  • Operators
  • Fundamentals of SQL
  • Creating Database
  • Creating Tables
  • Insertion of rows
  • Deletion of rows
  • Null Values
  • Removing Duplicate data
  • Sorting data
  • Alteration of Data

Introduction to SQL

Advance SQL

  • Joins
  • Relationships in SQL
  • Understanding Joins
  • Types of Joins
  • Subqueries
  • Functions
  • Brief about Functions
  • Aggregation Function
  • Data Warehousing concepts
  • Primary key/Unique key

Learn Python language

Introduction to Python

  • Introduction to Python Programming
  • Data types and Objects
  • Data Types in Python
  • Basic libraries in python
  • Functions in Python
  • Introduction to Loops
  • Conditional Operators

Objects in Python

  • Lists
  • Tuples
  • Sets
  • Dictionaries

Numpy- “Numeric python”- Package for mathematical computations

  • Importing Numpy
  • Mathematical functions and operators
  • Arrays
  • Reshaping
  • Indexing and Slicing
  • Sorting arrays
  • Statistical functions

Statistics for Business Analytics

Fundamentals of Statistics

  • Basic statistics; descriptive and summary
  • Inferential statistics
  • Statistical tests

Data Prep and Reduction techniques

  • Need for data preparation
  • Check Skewness of data
  • Outlier treatment
  • Missing values treatment

Basic Statistical Tests

  • Statistics Basics Introduction to Data Analytics and Statistical Techniques
  • Variable Distributions and Probability Distributions
  • Normal Distribution and Properties
  • Hypothesis Testing Null/Alternative Hypothesis formulation
  • P Value Interpretation
  • Correlation
  • T-Test
  • Chi-Sq test

Machine Learning in Python

Linear Regression Model

  • Basics of regression analysis
  • Correlation, VIF, missing value imputations and outliers
  • Create Linear regression model
  • Interpretation of results
  • Performance metrics for model.

Logistic Regression Model

  • Use cases of Logistic regression model.
  • Create a logistic regression model in Python
  • Churn prediction models and management
  • Feature Engineering- Feature Creation, Reduction and Selection
  • Sensitivity, specificity, Confusion matrix.
  • ROC curve.
  • Performance metrics of logistic regression


  • What is K-means clustering model
  • Create a clustering model in Python.
  • Interpreting results to select numbers of clusters for model.
  • Checking accuracy of the model.

Machine Learning in Python

Dimensions Reduction- Feature Engineering

  • Feature selection
  • Principal component Analysis

Advance ML technique

  • Time Series
  • Survival Analysis
  • Random Forest
  • GBM
  • Decision Trees
  • XgBoost

Introduction to AI, deep learning and other ML Techniques

  • CNN
  • RNN
  • LSTM
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