Data Science and Big Data Certified Course

  Course Description:

This Data Science and Big Data Certified Course is designed to equip learners with the essential skills and tools required to extract meaningful insights from vast volumes of data. The course blends foundational theories with practical applications, covering data wrangling, statistical analysis, machine learning, data visualization, and big data technologies.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After  Data Science and Big Data:

• Data Scientist
• Data Analyst
• Big Data Engineer
• Machine Learning Engineer
• Business Intelligence Analyst
• Data Engineer
• Statistician
• ML Specialist

Essential Skills you will Develop Data Science and Big Data:

• Data Analysis & Interpretation
• Statistical Modeling
• Programming with Python & R
• Machine Learning Algorithms
• Big Data Technologies

Tools Covered:

• Cloud Platforms
• AWS (Amazon EMR, S3)
• Google Cloud (Big Query)
• Data Engineering & Workflow Tools
• Apache Airflow
• Apache Kafka
• Data Analysis & Visualization Tools
• Jupiter Notebook
• Pandas, Jumpy

Syllabus:

Module 1: Introduction to Data Science & Big Data What is Data Science? Role of a Data Scientist Applications and Use Cases Introduction to Big Data and Its Characteristics (5Vs) Hadoop Ecosystem Overview.

Module 2: Python/R for Data Science Introduction to Python/R Data types, variables, functions, loops Data Structures (Lists, Tuples, Dictionaries, etc.) Libraries: NumPy, Pandas, Seaborn Notebook for coding.

Module 3: Data Preprocessing and Exploration Data cleaning and transformation
Handling missing data and outliers Data normalization and standardization
Exploratory Data Analysis (EDA) Feature engineering basics.

Module 4: Statistics and Probability for Data Science Descriptive Statistics
Probability Distributions Hypothesis Testing Sampling Techniques Inferential Statistics.

Module 5: Machine Learning Algorithms Supervised vs Unsupervised Learning
Regression (Linear, Logistic) Classification (Decision Trees, KNN, SVM) Clustering (K-Means, Hierarchical) Model Evaluation (Confusion Matrix, ROC-AUC).

Module 6: Big Data Tools & Technologies Hadoop HDFS and MapReduce Apache Hive & Pig Apache Spark Basics Spark ML lib for Machine Learning Real-time Data Processing with Kafka.

Module 7: Data Visualization and Communication Principles of Data Visualization
Tools: Tableau / Power BI / Python Creating Dashboards and Storytelling with Data
Case Study: Data-driven decision making.

Module 8: SQL and NoSQL Databases SQL Basics (Joins, Subqueries, Aggregates)
Advanced SQL for Data Analysis Introduction to NoSQL: MongoDB, Cassandra
Differences between SQL and NoSQL.

Module 9: Capstone Project (End-to-End Data Science Pipeline) Business Problem Understanding Data Collection, Cleaning, and Analysis Model Development and Evaluation Visualizing Results and Final Reporting Tools: Python, Spark, Tableau, SQL.

Module 10: Industry Trends & Career Guidance Real-world Applications in Finance, Healthcare, Retail, etc. Resume and Portfolio Building Interview Questions & Mock Interviews Certifications (like Google, IBM, AWS for Data Science) Freelancing and Entrepreneurship Opportunities.

Industry Projects:

• Customer Segmentation for Retail
• Predictive Analytics
• Fraud Detection
• Disease Prediction Using Patient Data
• Real-time Supply Chain Optimization
• Sentiment Analysis on Social Media Data

Who is this program for?

• Students and Fresh Graduates
• Working Professionals
• IT Professionals
• Researchers and Academicians
• Entrepreneurs and Business Analysts

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com