Foundations of Data Science

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 UNIT I

• Introduction to Data Science and Applications of data science, 
• Data scientist roles and responsibilities, skills needed to become a data scientist. 
• Need of Python for data analysis, 
• Introduction to Data Understanding and Pre-processing, Domain Knowledge, 
• Understanding structured and unstructured data. 
• Creation of synthetic dataset in MS Excel.

Reference Topics

• Synthetic Data  
• What is Synthetic Dataset
• Synthetic Data Creation
• Synthetic data generation a must have skill for new Data scientists                                                                                                

UNIT II

• Basics of Python programming: Variables, printing values, 
• If condition, arithmetic operations, 
• Loops - While, For. 
• Data Analysis process,  Data Analysis Practice
• Dataset generation, 
• Importing Dataset: Importing and Exporting Data, 
• Basic Insights from Datasets - Examples, 
• Cleaning and Preparing the Data: Identify and Handle Missing Values.

UNIT III

• Basics of essential Python libraries: 
• Introduction to NumPy, 
• Introduction to Pandas, 
• Introduction to Matplotlib, 
• Introduction to SciPy. 
• Data Processing, 
• Data Visualization, Basic Visualization Tools, 
• Specialized Visualization Tools, 
• Seaborn Creating and Plotting Maps.

UNIT IV

• Mathematical applications for data Analysis, 
• Scientific applications for data Analysis, 
• Basics of Supervised and Unsupervised Learning. 
• Decision Making. 
• Trend & predictive mining using Python, 
• Recommender systems.

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