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.
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.
Data Science Course Syllabus
Module 1: Data Science Project Lifecycle
- Demo: Introduction to Types of Analytics. Project Life Cycle, LMS walk through
Module 3: R Language
- R & R Studio
- Descriptive Stats in R
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 7: EDA
- Exploratory data analysis-I (Data Cleaning, Imputation Techniques, Data analysis)
- Visualization(Scatter Diagram, Correlation Analysis, Transformations)
Module 9: Logistic Regression
Module 11: Assignments
- Assignments Session-2(1 hr)
- Clustering introduction
- Hierarchical clustering
Module 13: Dimensional Reduction Techniques
- PCA
- tSNE
Module 15: Recommendation System and Assignment
- Recommender System
- Assignments Session-3 (1 hr)
Module 17: Decision Tree
- Decision Tree(C5.0)
Module 19: Feature Engineering
- Feature Engineering (Tree based methods, RFE,PCA)
Module 21: Ensembled Techniques
- Bagging
- Random Forest
- Boosting
- XGBM
- LGBM
Module 23: Regularization Techniques
- Lasso
- Ridge Regressions
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 27: Time Series
- Introduction to Time series
- Level, Trend and Seasonality Strategy
- Scatter plot
- Lag plot
- ACF
- Principles of Visualization
- Naïve forecasts
Module 29: Project Discussion
- Hands on using R and Python Projects description with deployment
Module 2: Basic Statistics
- Data Types
- Measure Of central tendency
- Measures of Dispersion
- Graphical Techniques
- Skewness & Kurtosis
- Box Plot
Module 4: Python
- Python (Installation and basic commands) and Libraries
- Jupyter note book
- Set up GitHub
- Descriptive Stats in Python
- Pandas and Matplotlib
Module 6: Hypothesis Testing
- Introduction to Hypothesis Testing
- Hypothesis Testing (2 proportion test, 2t sample t test)
- Anova
- Chisquare
Module 8: Linear Regression
- Principles of Regression
- Intro to Simple Linear Regression
- Multiple Linear Regression
Module 10: Deployment Methods
- Model deployments using R and Python
Module 12: Data Mining
- Unsupervised ML Algorithms
- K means
- DBSCAN
Module 14: Market Basket Analysis
- Association Rules
Module 16: Supervised Machine Learning
- Supervised Machine Learning Concept(Regression Tasks/ Classification Tasks)
Module 18: EDA-2
- EDA -2 (Encoding Methods – OHE, Label Encoders, Outlier detection-Isolation Forest)
- Calculating the Predictive Power Score (PPS)
Module 20: Modal Validation Techniques
- Model Validation Methods (train-test, CV, Shuffle CV, and Accuracy methods)
Module 22: Classifiers
- KNN
- Support Vector Machines
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 26: Naive Bayes
- Text classification using Naïve Bayes
- Emotion Mining
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
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 |
Timings |
|---|---|
| (Mon – Sat) 60 Days | 8:00 AM to 9:30 AM |
| (Mon – Sat) 60 Days | 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)
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.