Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.
Data Scientists are better statistician than computer programmer and better computer programmer than the Statistician. Students who come out of school education can shape their career by intersecting in this specialization to get the huge job opportunities in the data science field. After globalization, career evangelist comes up with a new era called “Data Era”.
Module 1: Introduction to Data Science Methodologies
Data types, Introduction to Data Science tools, Statistics, Approach to business problems, Numerical Categorical, R, Python, WEKA, RapidMiner, Hypothesis testing: Z, T, F test Anova, ChiSq
Module 2: Correlation/ Association Regression Categorical Variables
Introduction to Correlation Spearman Rank correlation, OLS Regression - Simple and Multiple Dummy Variables, Multiple Regression, Assumptions violation - MLE estimates, Using UCI ML repository dataset or built in R dataset
Module 3: Data Preparation
Data preparation and Variable identification, Advanced regression, Parameter Estimation/Interpretation, Robust Regression, Accuracy in Parameter Estimation, Using UCL, ML repository dataset or built in R dataset
Module 4: Logistic Regression
Introduction to Logistic Regression, Logit Function, Training-validation approach, Lift charts, Decline Analysis, Using UCL, ML repository dataset or built in R dataset
Module 5: Cluster Analysis Classification Models
Cluster Analysis Classification Models - Introduction to cluster Techniques, Distance Methodologies, Hierarchical and non- hierarchical Procedure, K-Means clustering, Introduction to decision trees/Segmentation with case study, Using UCL, ML repository dataset or built in R dataset.
Module 6: Introduction and to forecasting techniques
Introduction to Introduction to Time Series, Data and Analysis, Decomposition of Time Series, Trend and Seasonality detection and forecasting, Exponential Smoothing, Builting R Dataset, Sales forecasting Case Study
After completing this course, students will be able to appreciate the need of Data Science in day to day life.
They will be able to understand the process and components of Data Science project.
Student will the Learn importance of probability and statistics in Data Science
Who Should Attend?
Engineering and IT students
Graduates with a programming background
Data Sciences & AI Graduate Programmer
Senior Analyst, Data Scientist
Visual Analytics Analyst
Sr Product Engineer - Data Science
After completing this course and successfully passing the certification examination, the student will be awarded the “Introduction to Data Science” certification.
If a learner chooses not to take up the examination, they will still get a 'Participation Certificate'
Frequently Asked Questions
Course Features :
Mode Of Delivery:
Valid for 6 months post activation