Data Science Training

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Description of Data Science Training Course

Data science is an interdisciplinary field that combines techniques, tools, and methodologies from various domains, including statistics, mathematics, computer science, and domain expertise, to extract valuable insights and knowledge from data. It involves the process of collecting, cleaning, analyzing, and interpreting large and complex datasets to make informed decisions and solve real-world problems.

The data science training will accelerate your career as it covers relevant and trending topics to work in real-time scenarios for professionals. The online data science training program will help you to learn in a structured way to gain in-depth knowledge and earn your Data Science Certification for a peak in your career. Enroll now in the best data science course with iTrainU institute and get placed well in great companies.

Details

Duration :

3 Months

Class Hours :

1 hour/day

Duration :

1 hour/day

Certificate :

After completion of training

Study Material :

Books & Online Tests

Online Test :

Unlimited

Project :

Yes

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Data Science Course Syllabus

Module 1: Data Science Project Lifecycle

Demo: Introduction to Types of Analytics. Project Life Cycle, LMS walk through

Module 2: Basic Statistics

Data Types
Measure Of central tendency
Measures of Dispersion
Graphical Techniques
Skewness & Kurtosis
Box Plot

Module 3: R Language

R & R Studio
Descriptive Stats in R

Module 4: Python

Python (Installation and basic commands) and Libraries
Jupyter note book
Set up GitHub
Descriptive Stats in Python
Pandas and Matplotlib

Module 5: Basic Statistics-2

Random Variable
Probability
Probability Distribution
Normal Distribution
SND
Expected Value
Sampling Funnel
Sampling Variation
Central Limit Theorem
Confidence interval
Assignments Session-1 (1 hr)

Module 6: Hypothesis Testing

Introduction to Hypothesis Testing
Hypothesis Testing (2 proportion test, 2t sample t test)
Anova
Chisquare

Module 7: EDA

Exploratory data analysis-I (Data Cleaning, Imputation Techniques, Data analysis)
Visualization(Scatter Diagram, Correlation Analysis, Transformations)

Module 8: Linear Regression

Principles of Regression
Intro to Simple Linear Regression
Multiple Linear Regression

Module 9: Logistic Regression

Module 10: Deployment Methods

Model deployments using R and Python

Module 11: Assignments

Assignments Session-2(1 hr)
Clustering introduction
Hierarchical clustering

Module 12: Data Mining

Unsupervised ML Algorithms
K means
DBSCAN

Module 13: Dimensional Reduction Techniques

PCA
tSNE

Module 14: Market Basket Analysis

Association Rules

Module 15: Recommendation System and Assignment

Recommender System
Assignments Session-3 (1 hr)

Module 16: Supervised Machine Learning

Supervised Machine Learning Concept(Regression Tasks/ Classification Tasks)

Module 17: Decision Tree

Decision Tree(C5.0)

Module 18: EDA-2

EDA -2 (Encoding Methods – OHE, Label Encoders, Outlier detection-Isolation Forest)
Calculating the Predictive Power Score (PPS)

Module 19: Feature Engineering

Feature Engineering (Tree based methods, RFE,PCA)

Module 20: Modal Validation Techniques

Model Validation Methods (train-test, CV, Shuffle CV, and Accuracy methods)

Module 21: Ensembled Techniques

Bagging
Random Forest
Boosting
XGBM
LGBM

Module 22: Classifiers

KNN
Support Vector Machines

Module 23: Regularization Techniques

Lasso
Ridge Regressions

Module 24: Neural Network

ANN
Optimization Algorithm(Gradient descent)
Stochastic gradient descent(intro)
Back Propagation method
Introduction to CNN
Assignments Session-4 (1 hr)

Module 25: Text Mining

Introduction to Text Mining
VSM
Intro to word embedding’s
Word clouds and Document Similarity using cosine similarity
Named Entity Recognition

Module 26: Naive Bayes

Text classification using Naïve Bayes
Emotion Mining

Module 27: Time Series

Introduction to Time series
Level, Trend and Seasonality Strategy
Scatter plot
Lag plot
ACF
Principles of Visualization
Naïve forecasts

Module 28: Forecasting

Forecasting Error and it metrics
Model Based Approaches
AR Model for errors
AR Model for errors
Data driven approaches
MA
Exponential Smoothing
ARIMA
Survival Analysis

Module 29: Project Discussion

Hands on using R and Python Projects description with deployment

Upcoming Batch For Data Science Course

We are provide a flexible batch timing to our students. if you want to change a batch time let as know. we will schedule a batch according your flexible timing.

Duration

(Mon – Sat) 60 Days
(Mon – Sat) 60 Days

Timings

8:00 AM to 9:30 AM
8:00 PM to 9:30 PM

Data Science Course Certification

iTrainU institute offer the most detailed, practical-oriented, and the best data science course in Indore. Whether you are a beginner or have some experience, this course is the right fit for you as our expert trainers cover everything from latest trends of data science.

We tech you from Starting with Python programming and data analysis, and going deep down to machine learning, deep learning, and artificial intelligence (AI), you will learn all the concepts in a detail.

During this classroom course, you will master in data science skills, including data collection, extraction, integration, data mining, statistical analysis, predictive analysis, and numerous essential concepts. By the end of the training in data science, you will be on your way to kickstarting a thriving career as a successful Data Scientist.

Our Student's Placed

iTrainU Institute provide job assistance for placement , we have tie-up with many software Development companies like

Why ITRAINU Institute?

Practical Trainning approach
Real life example during lectures & live project
Lab Facility
Pearson VUE Exam Center
Redhat Linux Exam Center
Placement support
Corporate behaviour training
Basic ITSM training

Career counselling and road map for next 5 year.
Paid Hostel facility support
Live project Support for college Students
Internship support
Certified Trainers with Field Experience
Study Material
Latest Course Content
Recorded Lectures

Best Data Science Training Frequency Ask Question (FAQ)

What Is Data Science?

Data Science is a multidisciplinary field that involves extracting knowledge and insights from various types of data using techniques from statistics, computer science, domain expertise, and other relevant fields. It encompasses a range of activities, including data collection, data cleaning and preprocessing, data analysis, machine learning, and the interpretation and communication of results.

Can I Learn Data Science in 3 months?

Learning data science in three months is certainly possible, but the depth of your understanding and the complexity of the projects you can tackle might be limited by the short time frame.

What Are The Career Opportunity For Data Science professionals?

Is Data Science a good career option?