About the Program
The program on ‘Applied Data Science with Python’ covers a wide range of topics in applied data science, statistical intuitions, Python programming, data manipulation techniques, data engineering foundations, ML algorithms, data storytelling, visualizations, and model deployment. L&T collaborates with indus_x0002_try experts to ensure that the course content is up-to-date and relevant to current industry trends and practices. The course likely includes practical, hands-on projects that allow students to apply their learning in real-world scenarios.
Courses
    Credits Semester
  • Data Science Fundamentals and Statistical Intuitions with Python Programming 3 III
  • Data Manipulation and Analysis Techniques using Python Libraries 3 IV
  • Data-Engineering Foundations with SQL and Hadoop Eco System Tools 3 V
  • ML Algorithms Intuition and Model Building with Hyper-Parameter Tuning Techniques 3 V
  • Data Storytelling, Visualizations, and Model Deployment 3 VI
  • External Data Handling with Sqoop, Flume, and ETL Process with Hive 3 VII
  • Project Work - The Data Science Capstone: Exploring the Data Lifecycle with Best Practices 3 VIII
    Credits Semester
  • IT World Essentials: Your Digital Entrypoint 3 I
  • Critical Thinking, Design Thinking, Leadership and Teamwork 3 II
  • Project Work - The Data Science Capstone: Exploring the Data Lifecycle with Best Practices 3 VIII
    Credits Semester
  • Critical Thinking, Design Thinking, Leadership and Teamwork 3 II
  • Career Readiness in Digital Era 3 VI
Mode of Delivery
  • Self-paced learning – 12 hours
  • Expert sessions + Project work – 33 hours
  • Face-to-face instructor led sessions / VILT sessions (including project work) – 45 hours
  • Self-paced learning + Expert session – 45 hours
Job Roles
  • Machine Learning Engineer
  • Data Scientist
  • AI Research Scientist
  • Data Engineer
  • Data Science Consultant
  • Data Analyst
  • Business Intelligence Analyst
Software Tools
  • Python
  • Jupyter
  • SQL
  • Hadoop MapReduce
  • Apache Spark
  • Apache Pig
  • Apache Hive
  • Microsoft Excel
  • Scala
  • Power BI
  • PyTorch
  • Tableau
  • TensorFlow
Skills
  • Analyzing and interpreting data science concepts and statistical methods using Python.
  • Manipulating and cleaning datasets with Python libraries like pandas, NumPy, and SciPy.
  • Performing exploratory data analysis (EDA) and hypothesis testing to extract insights from data.
  • Implementing large-scale data processing using Hadoop ecosystem tools (HDFS, MapReduce, Hive).
  • Building and training machine learning models using algorithms like regression, classification, and clustering.
  • Visualizing data and presenting insights using Power BI, Tableau, and Python visualization libraries.
  • Deploying machine learning models to production environments using frameworks like Flask, Django, or FastAPI.
  • Handling external data integration with Sqoop, Flume, and managing ETL processes in Hadoop with Hive.