Name:

DATA ANALYSIS / DATA SCIENCE/ DATA ENGINEERING

Course Details:

Introduction to the Data Science Course at Adepts Institute

Unleash the Power of Data: Your Pathway to Success

In a world driven by data, the ability to analyze, interpret, and apply insights is not just a skill—it’s a superpower. Adepts Institute’s Data Science Course is meticulously designed to equip students and professionals with the tools they need to thrive in the fast-evolving digital landscape. Whether you’re a beginner or looking to upskill, this program offers a transformative experience that prepares you for real-world challenges.

Why Choose Adepts Institute for Data Science?

Key Highlights of the Course:

1. Comprehensive Curriculum:

   • Covering the full spectrum of data science topics: machine learning, deep learning, big data, AI, Data Engineering,  Python, R programming, data visualization, and more.

2. Industry-Relevant Skills:

   • Gain hands-on experience with real-world projects and case studies across multiple industries like finance, healthcare, and marketing.

3. Experts (Adepts):

   • Learn from industry veterans and academic experts who bring a wealth of knowledge and practical experience to the classroom.

4. Flexible Learning Options:

   • Choose from online, in-person, or hybrid formats tailored to fit your schedule.

5. Career Growth Support:

   • Benefit from career services, including resume workshops, mock interviews, and access to an extensive network of industry professionals.

6. Capstone Projects & Internships:

   • Work on impactful projects and gain real-world experience through industry immersion and internships.

7. Elective Specializations:

   • Customize your learning with electives in niche areas like Biostatistics, Econometrics, Financial Modeling, and Big Data Technologies.

Who Should Enroll?

• Students: Gain a cutting-edge education to start a career in data science.
• Professionals: Upskill and transition into high-demand data-centric roles.
• Entrepreneurs: Leverage data insights to make strategic business decisions.
• Tech Enthusiasts: Explore the transformative power of AI, ML, and predictive analytics.

Your Future Awaits

Join a community of innovative thinkers and problem-solvers who are shaping the future of data science. At Adepts Institute, you’ll receive not just education but also mentorship, guidance, and opportunities that position you for success in this lucrative field

FULL TRAINING TO BECOME A DATA SCIENTIST 

Part I

• ADEPT 111: Introduction to Data Science (3 Credits)
• ADEPT 113: Introduction to Python Programming (3 Credits)
• ADEPT 114: Linear Algebra (3 Credits)
• ADEPT 115: Calculus-I (3 Credits)
• ADEPT 116: Computational Statistics-I (3 Credits)
  
  

Part II

• ADEPT 121: Introduction to AI (3 Credits)
• ADEPT 122: Technical Communication Skills (3 Credits)
• ADEPT 123: Data Structures (3 Credits)
• ADEPT 125: Calculus-II (3 Credits)
• ADEPT 126: Computational Statistics-II (3 Credits)
• ADEPT 199: R-Programming (3 Credits)
  
  

Part III

• ADEPT 211: Programming Concepts (Using OOP/Java) (3 Credits)
• ADEPT 212: Data Analytics and Visualization (3 Credits)
• ADEPT 214: DBMS (3 Credits)
• ADEPT 215: Differential Equations (3 Credits)
• ADEPT 216: Discrete Mathematics (3 Credits)
  
  

Part IV

• ADEPT 221: Introduction to Machine Learning (3 Credits)
• ADEPT 222: Operating Systems (3 Credits)
• ADEPT 223: Statistical Inference (3 Credits)
• ADEPT 224: Scientific Computing (3 Credits)
• ADEPT 225: Multivariate Analysis (3 Credits)
• ADEPT 299: Project (3 Credits)
  
  

Part V

• ADEPT 311: Data Governance (3 Credits)
• ADEPT 313: Algorithm Analysis (3 Credits)
• ADEPT 314: Parallel Computing (3 Credits)
• ADEPT 315: Optimization Techniques (3 Credits)
• ADEPT 316: Statistical and Digital Literacy (3 Credits)
  
  

Part VI

• ADEPT 321: Feature Engineering (3 Credits)
• ADEPT 322: Big Data Technologies (3 Credits)
• ADEPT 323: Data Security (3 Credits)
• ADEPT 324: Data Science in Digital Marketing (3 Credits)
• ADEPT 325: Time Series Analysis (3 Credits)
• ADEPT 399: Project (3 Credits)
  
  

Part VII

• ADEPT 411: Deep Learning (3 Credits)
• ADEPT 412: Predictive Analytics (3 Credits)
• ADEPT 413: Applied Data Science (3 Credits)
• ADEPT 415: Industry Immersion (3 Credits)
• ADEPT Elective I: Elective I (3 Credits)
• ADEPT Elective II: Elective II (3 Credits)
  
  

Part VIII

• ADEPT 498: Internship / Final Project (6 Credits)
• ADEPT 499: Final Report 
  
  

Elective Courses

• Biomathematics
• Health Informatics
• Econometrics
• Numerical Methods in ODE
• Data Mining
• Bioinformatics
• Stochastic Models
• Mathematical Modeling
• Statistical Modeling
• Biostatistics
• Industrial Statistics
• Agricultural Statistics
• Population Dynamics
• Financial Modeling

DATA ENGINEERING OPTION

• 1. Introduction to Data Engineering

  • Overview of Data Engineering:
    • Roles and responsibilities of a Data Engineer.
    • Importance of Data Engineering in the data lifecycle.
  • Core Concepts:
    • Structured, semi-structured, and unstructured data.
    • Batch vs. Stream processing.
    • ETL (Extract, Transform, Load) vs. ELT pipelines.
  • Tools Overview:
    • Overview of common tools (SQL, Python, Apache Spark, Airflow, etc.).

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  2. Fundamentals of Data Modeling

  • Understanding Data Models:
    • Conceptual, Logical, and Physical Data Models.
    • Data Normalization and Denormalization.
  • Data Warehousing Principles:
    • Star Schema vs. Snowflake Schema.
    • Fact and Dimension Tables.
  • Data Lakes vs. Data Warehouses:
    • Purpose and differences.
    • Use cases for each.

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  3. Relational Databases and SQL

  • Introduction to Databases:
    • Relational vs. Non-Relational Databases.
    • ACID properties.
  • SQL Fundamentals:
    • Basic queries: SELECT, INSERT, UPDATE, DELETE.
    • Joins, Aggregations, and Subqueries.
    • Indexing and Performance Optimization.
  • Advanced SQL:
    • Window Functions.
    • Common Table Expressions (CTEs).
    • Handling large datasets.

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  4. Programming for Data Engineering

  • Python for Data Engineering:
    • Data manipulation with Pandas.
    • File handling (CSV, JSON, Parquet).
    • Regular Expressions and Error Handling.
  • Introduction to Scala/Java (optional, for Spark users).
  • Shell Scripting Basics:
    • Automating repetitive tasks.

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  5. Big Data Ecosystems

  • Introduction to Big Data:
    • Characteristics (Volume, Variety, Velocity, Veracity).
    • Hadoop ecosystem overview.
  • Apache Spark:
    • Architecture and components.
    • DataFrames and RDDs.
    • SparkSQL and PySpark.
  • Distributed Systems:
    • Basics of distributed computing.
    • Challenges like consistency, partitioning, and fault tolerance.

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  6. Data Pipelines and Workflow Automation

  • ETL and ELT Processes:
    • Best practices for building pipelines.
    • Error handling and retries.
  • Apache Airflow:
    • DAGs (Directed Acyclic Graphs).
    • Task dependencies and scheduling.
  • Modern Alternatives:
    • Prefect, Luigi, or Dagster.

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  7. Data Storage and Management

  • Storage Systems:
    • Relational (PostgreSQL, MySQL).
    • NoSQL (MongoDB, Cassandra, HBase).
    • Cloud storage (AWS S3, Google Cloud Storage, Azure Blob).
  • Data Partitioning and Indexing:
    • Partitioning strategies.
    • Importance of indexing for performance.
  • File Formats:
    • CSV, JSON, Parquet, ORC, Avro.

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  8. Data Streaming

  • Introduction to Streaming:
    • Batch vs. Stream processing.
  • Apache Kafka:
    • Architecture and components (producers, consumers, brokers).
    • Use cases for Kafka.
  • Stream Processing Tools:
    • Spark Streaming.
    • Apache Flink.

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  9. Cloud Computing and Data Engineering

  • Introduction to Cloud Platforms:
    • AWS, Google Cloud Platform, Microsoft Azure.
  • Cloud-based Data Tools:
    • AWS Redshift, Google BigQuery, Azure Synapse Analytics.
  • Serverless Architectures:
    • Lambda Functions and Cloud Functions.
    • Hands-on with Dataflow or Glue.

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  10. Data Engineering Best Practices

  • Data Governance:
    • Data quality, security, and compliance.
    • GDPR and CCPA guidelines.
  • Version Control and Collaboration:
    • Using Git for data engineering projects.
  • Scalability and Performance Tuning:
    • Optimization techniques for pipelines and databases.

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  11. Capstone Project

  • Design and Implement a Complete Data Pipeline:
    • Use real-world datasets (e.g., from Kaggle or open data sources).
    • Create an end-to-end pipeline from data ingestion to transformation and reporting.
    • Utilize cloud services for deployment.

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  12. Interview Preparation and Industry Trends

  • Interview Preparation:
    • Common Data Engineering interview questions.
    • Mock interview practice.
  • Emerging Trends:
    • Data Mesh, DataOps, and Observability.
    • Integration with AI/ML pipelines.