Professional Certificate in Data Science & Business Analytics

The Professional Certificate in Data Science & Business Analytics is an intensive 11-month industry-aligned program designed to equip learners with complete mastery across modern data science, analytics, machine learning, artificial intelligence, and business intelligence technologies. This program delivers a full end-to-end learning journey — from foundational concepts to advanced AI systems — preparing learners for high-demand careers in Data Science, Business Analytics, Machine Learning Engineering, Data Engineering, BI Development, and AI Research.

Module 1: Diving into the World of Data

The program begins with essential data foundations, helping you build conceptual clarity in:

• Data, Data Analytics, and Data Science

• Data Science tools, methodology, and job roles

• Types of data: continuous vs. categorical

• Data visualization & interpretation techniques

• Descriptive and inferential statistics

This module ensures you fully understand how data works, how it is interpreted, and how insights are generated.

Module 2: SQL Essentials (22 Hours)

SQL is the backbone of all analytics roles. This module builds strong command over relational databases, including:

• DDL, DML, DQL

• SELECT, WHERE, LIKE, ORDER BY, DISTINCT

• Comparison, logical, arithmetic, range & list operators

• SQL functions: String, Math, Date, Aggregate

• GROUP BY & HAVING

• Joins (INNER, LEFT, RIGHT, FULL)

• Subqueries & search conditions

By the end, learners can write advanced queries and handle enterprise-level datasets with confidence.

Module 3: Python Essential + Data Science Libraries (52 Hours)

Python is the most in-demand programming language in data science. This module covers:

• Python fundamentals, variables, syntax

• Data types: String, Tuple, List, Dictionary, Set

• Loops, conditional logic, list comprehensions

• User-defined, anonymous (lambda), and recursive functions

• File handling

• Object-Oriented Programming (OOP): classes, inheritance, abstraction, polymorphism

• Regular Expressions (RegEx) & Web Scraping (requests, BeautifulSoup)

Python for Data Science Libraries:

• NumPy: arrays, vectorization, slicing, broadcasting

• Pandas: Series, DataFrames, indexing, filtering, reading data

• Matplotlib & Seaborn: bar charts, scatter plots, heat maps, histograms, box plots, pie charts, and more

This module ensures complete readiness for real-world data preprocessing, manipulation, and visualization tasks.

Module 5: Machine Learning (36 Hours)

A complete practical journey into the world of ML:

• Unsupervised Learning: K-Means, PCA

• EDA & Preprocessing: handling missing values, skewness, outliers, encoding, normalization

• Regression: Linear, Polynomial, cost functions, OLS, Ridge, Lasso

• Classification: Logistic Regression, Confusion Matrix, Decision Trees, Random Forest, Boosting (AdaBoost, Gradient Boosting, XGBoost)

• Support Vector Machines (SVM): support vectors, kernels, hard vs. soft margin

Learners gain hands-on experience building ML models used in real business applications.

Module 6: Tableau (26 Hours)

This module trains learners in the world’s most popular BI visualization tool:

• Tableau architecture & products

• Data extracts vs. live connections

• Data blending & joins

• Discrete vs. continuous measures

• Advanced visualizations: heatmaps, tree maps, bullet charts, dual-axis, scatter plots, box plots, Gantt charts

• LOD expressions, calculated fields, parameters

• Trend lines, forecasting, reference lines

• Dashboard building, publishing, interactive actions

• Real-world IPL Data Analysis Case Study

Graduates are fully prepared for BI and dashboard development roles.

Module 7: Artificial Intelligence, Deep Learning & Computer Vision (47 Hours)

A cutting-edge immersion into modern AI systems:

• Deep Learning vs. ML vs. AI

• Activation functions & gradient descent

• Artificial Neural Networks (ANNs)

• Convolutional Neural Networks (CNNs)

• OpenCV for image/video processing

• Object detection: Haar Cascade, MobileNet SSD, YOLO, RCNN

• MediaPipe models (FaceMesh, Pose, Hand, Holistic)

Natural Language Processing (NLP):

• Tokenization, bag of words, stopword removal, stemming, lemmatization

• POS tagging & Named Entity Recognition

• Text classification: TF-IDF, CountVectorizer + ANN

• Sentiment analysis

• Sequence models: RNN, LSTM, GRU

• Transformers & Chatbots using RASA

SAS Programming + Time Series Analysis

The final module introduces enterprise-level SAS and advanced forecasting techniques:

• Time Series: ACF, PACF, ARIMA, seasonality

• Stock prediction using RNN & LSTM

• SAS datasets, libraries, data steps

• SQL in SAS

• Reporting tools: PRINT, REPORT, TABULATE

• Data cleaning, merging, transformations

• Debugging & iterative processing

Why This Program Is Career-Transformative

• Covers entire data science pipeline from basics to advanced AI

• Hands-on learning with real projects & case studies

• Aligns with top analytics job roles globally

• Prepares you for high-demand fields like ML, BI, AI, and Big Data

This certification gives you the technical, analytical, and real-world project experience required to become a job-ready data professional.