Data Genius

What do you Learn?

Acquaintance Centro Overview

This advanced 6-month course provides a comprehensive education in data science, data analytics, and big data, covering foundational principles, statistical analysis, machine learning, big data technologies, and advanced topics. It equips students with the skills needed to work with data, derive insights, and apply data science in real-world scenarios.


Foundations of Data Science and Analytics

1.1. Introduction to Data Science

  • Understanding Data Science and its Applications
  • The Data Science Lifecycle
  • Tools and Environments (Python, Jupyter, R)

1.2. Data Preprocessing and Exploration

  • Data Cleaning and Transformation
  • Exploratory Data Analysis (EDA)
  • Data Visualization (Matplotlib, Seaborn)


Statistics and Machine Learning

2.1. Statistical Analysis for Data Science

  • Descriptive and Inferential Statistics
  • Hypothesis Testing
  • Probability Distributions

2.2. Supervised Learning

  • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests


Unsupervised Learning and Clustering

3.1. Unsupervised Learning

  • Principal Component Analysis (PCA)
  • K-Means Clustering
  • Hierarchical Clustering

3.2. Natural Language Processing (NLP)

  • Text Processing and Tokenization
  • Sentiment Analysis
  • Text Classification


Big Data Technologies

4.1. Introduction to Big Data

  • Characteristics of Big Data
  • Hadoop Ecosystem (HDFS, MapReduce)
  • Apache Spark Fundamentals

4.2. Big Data Processing with Spark

  • Spark RDDs and DataFrames
  • Spark SQL and MLlib
  • Real-time Data Processing with Spark Streaming


Data Analytics and Advanced Topics

5.1. Data Analytics with Pandas

  • Data Manipulation and Analysis
  • Time Series Analysis
  • Data Visualization with Seaborn

5.2. Deep Learning

  • Neural Networks and Deep Learning Basics
  • Deep Learning Frameworks (TensorFlow, Keras)
  • Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)


Capstone Projects and Advanced Topics

6.1. Capstone Project Development

  • Acquaintees work on real-world data science projects
  • Mentoring and Guidance from Specialized Instructors

6.2. Big Data Processing in Real-world Scenarios

  • Big Data in Industry (e.g., finance, healthcare)
  • Real-world Case Studies
  • Ethical Considerations in Data Science

6.3. Data Engineering and Data Warehousing

  • ETL (Extract, Transform, Load) Processes
  • Data Warehousing Concepts
  • Building Data Pipelines

6.4. Final Projects Presentation and Evaluation

  • Acquaintees present and evaluate their capstone projects
  • Feedback and Assessment

6.5. Course Conclusion and Future Directions

  • Review of Key Concepts
  • Emerging Trends in Data Science and Big Data
  • Experience Certification
  • Placement Training and Interview grooming