Data Science Training - Data Science Course In Ahmedabad

Data Science Course And Training With 100% Job Assistance

About Data Science Training Institute in Ahmedabad

Desire Infotech provides Data Science course in Ahmedabad, the most comprehensive Data Science course in the market, covering the complete Data Science lifecycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, Tableau, Spark, Hadoop, programming languages like R programming, Python are covered extensively as part of this Data Science training. Desire Infotech is considered as the best Data Science training institute in Ahmedabad which offers services from training to placement as part of the Data Science training program.

Why Should You Choose Desire Infotech For Data Science Training In Ahmedabad?

Desire Infotech is a global institute for data science in Ahmedabad rendering specialized coaching in the fields such as Data Science, Machine Learning, Deep Learning, Artificial Intelligence, the Internet of Things and Python Learning.

Desire Infotech starting its training from basic to advanced. In addition, whole training conducted by Industry Expert who has been working in the same field for more than 5 years. Last but the least, one to one focus is there.Desire Infotech is considered to be one of the best Data Science training institutes in Ahmedabad.

Data Science Courses in Ahmedabad: FAQs

What is the average salary of a data scientist after data science courses?

Ans. The average salary for a data scientist in India is INR 3 LPA. Data Scientists are at the moment gaining from the big data trend with analytics professionals making 23% higher as compared to an average software engineer in India after graduating from data science courses.

Is the Data Science course still in demand?

Ans. Yes, a Data Science course has always been and is still in demand.

What is data science course eligibility?

Ans. Anyone who has completed 10+2 from a recognized board is eligible to pursue a data science course. They must’ve studied science with maths as a compulsory subject and scored more than 50% marks in each of the subjects in the class 12th exam.

Data Science Course Syllabus

STATISTICS ESSENTIALS FOR ANALYTICS

All the topics in the following section will explain the basics of what it is, which scenario you want to use, What math behind it, How to implement with an analytic tool, what inferences you are getting from the final result

Understanding the Data

Probability and its Uses

Statistical Inference

Data Clustering

Testing the Data

Regression Modelling

Data Science with Python

Module 1: Introduction to Data Science

What is Data Science?

What is Machine Learning?

What is Deep Learning?

What is AI?

Data Analytics & it’s types

Module 2: Introduction to Python

What is Python?

Why Python?

Installing Python

Python IDEs

Jupyter Notebook Overview

Module 3: Python Basics

Python Basic Data types

Lists

Slicing

IF statements

Loops

Dictionaries

Tuples

Functions

Array

Selection by position & Labels

Module 4: Python Packages

Pandas

Numpy

Sci-kit Learn

Mat-plot library

Module 5: Importing data

Reading CSV files

Saving in Python data

Loading Python data objects

Writing data to csv file

Module 6: Manipulating Data

Selecting rows/observations

Rounding Number

Selecting columns/fields

Merging data

Data aggregation

Data munging techniques

Module 7: Statistics Basics

• Central Tendency

Mean

Median

Mode

Skewness

Normal Distribution

Probability Basics

What does mean by probability?

Types of Probability

ODDS Ratio?

Standard Deviation

Data deviation & distribution

Variance

Bias variance Trade off

Underfitting

Overfitting

Distance metrics

Euclidean Distance

Manhattan Distance

Outlier analysis

What is an Outlier?

Inter Quartile Range

Box & whisker plot

Upper Whisker

Lower Whisker

Scatter plot

Cook’s Distance

Missing Value treatments

What is a NA?

Central Imputation

KNN imputation

Dummification

Correlation

Pearson correlation

Positive & Negative correlation

Module 10: Unsupervised Learning

K-Means

K-Means ++

Hierarchical Clustering

Module 11: Other Machine Learning algorithms

K – Nearest Neighbour

Naïve Bayes Classifier

Decision Tree – CART

Decision Tree – C50

Random Forest