Certificate in Data Science
Enrol Data Science with us
Data science is shaping the future, and we’re here to ensure you’re part of it! Here’s why we stand out:
✅ Expert Guidance: Learn from industry leaders with real-world experience in AI, machine learning, and big data.
✅ Hands-On Learning: Tackle real-world projects and case studies to build a portfolio that sets you apart.
✅ Cutting-Edge Tools: Gain mastery in Python, R, SQL, and the latest data visualization and machine learning tools.
✅ Job-Ready Skills: Our curriculum focuses on practical skills, ensuring you’re ready for a booming job market.
Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.
It is ultimately about using data in creative ways to generate business value
A Data Scientist does the exploratory analysis to discover insights from the data
So, Data Science is primarily used to make decisions and predictions
Data Science – The Future
28% Rise in demand for Data Scientists by 2020
3 out of 5 Highest paying professionals are related to Data Science and AI
36% Higher base salary is what the Data scientists earn compared to other analytics professionals.
IT Industry A Changing Scenario
The global IT services market is expected to grow by over $1160 Billion in 2020
Worldwide IT spending is forecast to reach $2.7 Trillion in 2020
This shows the immense potential in the IT industry for a lucrative career
The IT industry is experiencing a revolution with streams like Data Science, Machine Learning, Artificial Intelligence, and Software Testing coming up rapidly
These are the jobs of the future and will continue to be in great demand
Software Trained: MS Office, Python, MongoDB, R Studio
Job Profile: Entry Level Data Analyst
Learning Objectives:
- Work with spreadsheets and analyze data using MS Excel
- Learn the basics of social media, mobile technology, analytics, and cloud computing along with an understanding of their interconnectivity
- Learn the basic concepts of object-oriented programming
- Learn how to program with the popular development language, Python
- Build Web applications using Python
- Learn MongoDB concepts, features, architecture, and data model, and how to install, configure and monitor open-source databases
- Master data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language
- Develop a real-world application using the R language
Study Materials: Students will get a printable softcopy through Onlivarsity.com.
Entry Requirements: Since these are professional qualifications in programming, a person should have knowledge of basic computer applications and an understanding of programming languages like C, C++, and Java.
Award of Certifications: After the completion/passing of each exam module, the candidate will get certification from APTECH Computer Education, India.
Step 1: Programming principles and techniques & SMAC
Step 2: Introduction to Data Science & Basic Statistics
Step 3: Python for Data Science
Key Learning Objectives:
Write your first Python program by implementing concepts of variables, strings, functions, loops, and conditions. Understand the nuances of lists, sets, dictionaries, conditions and branching, and objects and classes. Work with data in Python such as reading and writing files, loading, working, and saving data with Pandas.
Lesson 01 – Python Basics
Lesson 02 – Python Data Structures
Lesson 03 – Python Programming Fundamentals
Lesson 04 – Working with Data in Python
Lesson 05 – Working with NumPy Arrays
Step 4: Data Science with R
Key Learning Objectives:
Gain a foundational understanding of business analytics Install R, R-studio, and workspace setup, and learn about the various R packages Master R programming and understand how various statements are executed in R Gain an in-depth understanding of data structure used in R and learn to import/export data in R Define, understand and use the various apply functions and DPYR functions Understand and use the various graphics in R for data visualization Gain a basic understanding of various statistical concepts Understand and use hypothesis testing method to drive business decisions Understand and use linear, non-linear regression models, and classification techniques for data analysis Learn and use the various association rules and Apriori algorithm Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering.
Lesson 01 – Introduction to Business Analytics
Lesson 02 – Introduction to R Programming
Lesson 03 – Data Structures
Lesson 04 – Data Visualization
Lesson 05 – Statistics for Data Science I
Lesson 06 – Statistics for Data Science II
Lesson 07 – Regression Analysis
Lesson 08 – Classification
Lesson 09 – Clustering
Lesson 10 – Association
Step 5: Data Science with Python
Key Learning Objectives:
Gain an in-depth understanding of Data Science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics Install the required Python environment and other auxiliary tools and libraries. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions. Perform high-level mathematical computing using the NumPy package and its vast library of mathematical functions. Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave Perform data analysis and manipulation using data structures and tools provided in the Pandas package Gain expertise in Machine Learning using the Scikit-Learn package Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline.
Lesson 01 – Data Science Overview
Lesson 02: Data Analytics Overview
Lesson 03: Statistical Analysis and Business Applications
Lesson 04: Python Environment Setup and Essentials
Lesson 05: Mathematical Computing with Python (NumPy)
Lesson 06 – Scientific computing with Python (Scipy)
Lesson 07 – Data Manipulation with Pandas
Lesson 08 – Machine Learning with Scikit–Learn
Lesson 09 – Natural Language Processing with Scikit Learn
Lesson 10 – Data Visualization in Python using matplotlib This lesson teaches you to visualize data in python using matplotlib and plot them.
Lesson 11 – Web Scraping with BeautifulSoup
Lesson 12 – Python integration with Hadoop MapReduce and Spark
Step 6: SQL (MySQL) and NoSQL (MongoDB)
Key Learning Objectives:
Understand databases and relationships Use common query tools and work with SQL commands Understand transactions, creating tables and views Comprehend and execute stored procedures.
Lesson 1- Fundamental SQL Statements
Lesson 2-Restore and Back-up
Lesson 3-Selection Commands: Filtering
Lesson 4-Selection Commands: Ordering
Lesson 5-Alias
Lesson 6-Aggregate Commands
Lesson 7-Group By Commands
Lesson 8-Conditional Statement
Lesson 9-Joins
Lesson 10-Subqueries
Lesson 11-Views and Index
Lesson 12-String Functions
Lesson 13-Mathematical Functions
Lesson 14-Date – Time Functions
Lesson 15-Pattern (String) Matching
Lesson 16-User Access Control Functions
Step 7: Machine Learning
Key Learning Objectives:
Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modeling Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises Acquire thorough knowledge of the statistical and heuristic aspects of Machine Learning Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python Validate Machine Learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques Comprehend the theoretical concepts and how they relate to the practical aspects of Machine Learning.
Lesson 01 – Introduction to Artificial Intelligence and Machine Learning
Lesson 02: Data Wrangling and Manipulation
Lesson 03: Supervised Learning
Lesson 04: Feature Engineering
Lesson 05: Supervised Learning-Classification
Lesson 06: Unsupervised learning
Lesson 07: Time Series Modelling
Lesson 08: Ensemble Learning
Lesson 09: Recommender Systems
Lesson 10: Text Mining
Step 8: Tableau
Key Learning Objectives:
Become an expert on visualization techniques such as heat map, treemap, waterfall, Pareto Understand metadata and its usage Work with Filter, Parameters, and Sets Master special field types and Tableau-generated fields and the process of creating and using parameters Learn how to build charts, interactive dashboards, story interfaces, and how to share your work Master the concepts of data blending, create data extracts and organize and format data Master arithmetic, logical, table, and LOD calculations
Lesson 01 – Getting Started with Tableau
Lesson 02 – Core Tableau in Topics
Lesson 03 – Creating Charts in Tableau
Lesson 04 – Working with Metadata
Lesson 05 – Filters in Tableau
Lesson 06 – Applying Analytics to the worksheet
Lesson 07 – Dashboard in Tableau
Lesson 08 – Modifications to Data ConnectionsLesson 09 – Introduction to Level of Details in Tableau (LODS)
Step 9: Project
Key Learning Objectives:
Data Processing – In this step, you will apply various data processing techniques to make raw data meaningful. Model Building – You will leverage techniques such as regression and decision trees to build Machine Learning models that enable accurate and intelligent predictions. You may explore Python, R to build your model. You will follow the complete model-building exercise from data split to test and training and validating data using the k-fold crossvalidation process. Model Fine-tuning – You will apply various techniques to improve the accuracy of your model and select the champion model that provides the best accuracy. Dashboarding and Representing Results – As the last step, you will be required to export your results into a dashboard with meaningful insights using Tableau.
