Associate in Data Science

Introduction To Python

• Installation and Working with Python
• Understanding Python variables
• Python basic Operators
• Understanding the Python blocks.
• Version Control with Git & GitHub

Python Keyword and Identiers

• Python Comments, Multiline Comments.
• Python Indentation
• Understating the concepts of Operators
  • Arithmetic
  • Relational
  • Logical
  • Assignment
  • Membership
  • Identity

Introduction To Variables

• Variables, expression condition and function
• Global and Local Variables in Python
• Packing and Unpacking Arguments
• Type Casting in Python
• Byte objects vs. string in Python
• Variable Scope

Python Data Type

• Declaring and using Numeric data types
• Using string data type and string operations
• Understanding Non-numeric data types
• Understanding the concept of Casting and Boolean.
• Strings
• List
• Tuples
• Dictionary
• Sets

Control Structure & Flow

• Statements ? if, else, elif
• How to use nested IF and Else in Python
• Loops
• Loops and Control Statements.
• Jumping Statements ? Break, Continue, pass
• Looping techniques in Python
• How to use Range function in Loop
• Programs for printing Patterns in Python
• How to use if and else with Loop
• Use of Switch Function in Loop
• Elegant way of Python Iteration
• Generator in Python
• How to use nested Loop in Python
• Use If and Else in for and While Loop
• Examples of Looping with Break and Continue Statement
• How to use IN or NOT IN keyword in Python Loop.

Python Function, Modules and Packages

• Python Syntax
• Function Call
• Return Statement
• Arguments in a function ? Required, Default, Positional, Variable-length
• Write an Empty Function in Python ?pass statement.
• Lamda/ Anonymous Function
• *args and **kwargs
• Help function in Python
• Scope and Life Time of Variable in Python Function
• Nested Loop in Python Function
• Recursive Function and Its Advantage and Disadvantage
• Organizing python codes using functions
• Organizing python projects into modules
• Importing own module as well as external modules
• Understanding Packages
• Random functions in python
• Programming using functions, modules & external packages
• Map, Filter and Reduce function with Lambda Function
• More example of Python Function

List

• What is List.
• List Creation
• List Length
• List Append
• List Insert
• List Remove
• List Append & Extend using ?+? and Keyword
• List Delete
• List related Keyword in Python
• List Revers
• List Sorting
• List having Multiple Reference
• String Split to create a List
• List Indexing
• List Slicing
• List count and Looping
• List Comprehension and Nested Comprehension

Tuple

• What is Tuple
• Tuple Creation
• Accessing Elements in Tuple
• Changing a Tuple
• Tuple Deletion
• Tuple Count
• Tuple Index
• Tuple Membership
• TupleBuilt in Function (Length, Sort)

Dictionary

• Dict Creation
• Dict Access (Accessing Dict Values)
• Dict Get Method
• Dict Add or Modify Elements
• Dict Copy
• Dict From Keys.
• Dict Items
• Dict Keys (Updating, Removing and Iterating)
• Dict Values
• Dict Comprehension
• Default Dictionaries
• Ordered Dictionaries
• Looping Dictionaries
• Dict useful methods (Pop, Pop Item, Str , Update etc.)

Sets

• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations
• Frozen Sets

Strings

• What is Set
• Set Creation
• Add element to a Set
• Remove elements from a Set
• PythonSet Operations

Python Exception Handling

• Python Errors and Built-in-Exceptions
• Exception handing Try, Except and Finally
• Catching Exceptions in Python
• Catching Specic Exception in Python
• Raising Exception
• Try and Finally

Python File Handling

• Opening a File
• Python File Modes
• Closing File
• Writing to a File
• Reading from a File
• Renaming and Deleting Files in Python
• Python Directory and File Management
• List Directories and Files
• Making New Directory
• Changing Directory

Python Database Interaction

• Basic SQL, DDL and DML commands
• SQL Database connection using
• Creating and searching tables
• Reading and Storing cong information on database
• Programming using database connections

Reading an excel

• Working With Excel
• Reading an excel le using Python
• Writing to an excel sheet using Python
• Python| Reading an excel le
• Python | Writing an excel le
• Adjusting Rows and Column using Python
• ArithmeticOperation in Excel le.
• Play with Workbook, Sheets and Cells in Excel using Python
• Creating and Removing Sheets
• Formatting the Excel File Data
• More example of Python Function

Complete Understanding of OS Module of Python

• Check Dirs. (exist or not)
• How to split path and extension
• How to get user prole detail
• Get the path of Desktop, Documents, Downloads etc.
• Handle the File System Organization using OS
• How to get any les and folder?s details using OS

Data Analysis and Visualization using Pandas.

• Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
• How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
• Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalue?s
• Exploring the Data Plotting, Correlations, and Histograms
• Getting statistical information about the data Analysis Concepts, Handle the None Values
• Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
• Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
• Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
• Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
• Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
• Exporting the results to Excel Joins
• Python | Pandas Data Frame Inner Join
• Under sting the properties of Data Frame Left Join (Left Outer Join)
• Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
• Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
• Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
• Pandas | How to Group Data How to apply Lambda / Function on Data Frame
• Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)

Data Analysis and Visualization using NumPy

• Introduction to NumPy Numerical Python
• Importing NumPy and Its Properties
• NumPy Arrays
• Creating an Array from a CSV
• Operations an Array from a CSV
• Operations with NumPy Arrays
• Two-Dimensional Array
• Selecting Elements from 1-D Array
• Selecting Elements from 2-D Array
• Logical Operation with Arrays
• Indexing NumPy elements using conditionals
• NumPy?s Mean and Axis
• NumPy?s Mode, Median and Sum Function
• NumPy?s Sort Function and More

Data Analysis and Visualization using MatPlotLib

• Bar Chart using Python MatPlotLib
• Column Chart using Python MatPlotLib
• Pie Chart using Python MatPlotLib
• Area Chart using Python MatPlotLib
• Scatter Plot Chart using Python MatPlotLib
• Play with Charts Properties Using MatPlotLib
• Export the Chart as Image
• Understanding plt. subplots () notation
• Legend Alignment of Chart using MatPlotLib
• Create Charts as Image
• Other Useful Properties of Charts.
• Complete Understanding of Histograms
• Plotting Different Charts, Labels, and Labels Alignment etc.

Introduction to Data Visualization with Seaborn

• Introduction to Seaborn
• Making a scatter plot with lists
• Making a count plot with a list
• Using Pandas with seaborn
• Tidy vs Untidy data
• Making a count plot with a Dataframe
• Adding a third variable with hue
• Hue and scattera plots
• Hue and count plots
• Visualizing Two Quantitative Variables
  • Introduction to relational plots and subplots
  • Creating subplots with col and row
  • Customizing scatters plots
  • Changing the size of scatter plot points
  • Changing the style of scatter plot points
  • Introduction to line plots
  • Interpreting line plots
  • Visualizing standard deviation with line plots
  • Plotting subgroups in line plots
• Visualizing a Categorical and a Quantitative Variable
  • Current plots and bar plots
  • Count plots
  • Bar plot with percentages
  • Customizing bar plots
  • Box plots
  • Create and interpret a box plot
  • Omitting outliers
  • Adjusting the whisk
  • Point plots
  • Customizing points plots
  • Point plot with subgroups
• Customizing Seaborn Plots
  • Changing plot style and colour
  • Changing style and palette
  • Changing the scale
  • Using a custom palette
  • Adding titles and labels Part 1
  • Face Grids vs. Axes Subplots
  • Adding a title to a face Grid object
  • Adding title and labels Part 2
  • Adding a title and axis labels
  • Rotating x-tics labels
  • Putting it all together
  • Box plot with subgroups
  • Bar plot with subgroups and subplots
  • Well done! What?s next

Foundation for AI: Learn traditional ML models, evaluation, and workflows.

• Introduction to ML, AI, and Deep Learning
• Types of ML (Supervised, Unsupervised, Reinforcement)
• ML Pipeline: Data Cleaning, Feature Engineering
• Common ML Algorithms: Linear, Logistic, DT, RF, SVM, KNN
• Model Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
• Overfitting, Underfitting, Cross-Validation
• Hands-on Project: Titanic Dataset (or similar)

Understand the inner workings of neural networks and train them with Keras.

• Introduction to Neural Networks & Deep Learning
• Activation Functions (ReLU, Sigmoid, Tanh)
• Feedforward Neural Network
• Backpropagation & Gradient Descent
• Learning Rate, Schedulers & Optimizers (SGD, Adam, RMSProp)
• Softmax, Cross-Entropy Loss
• Keras Basics: Sequential API & Functional API
• Fully Connected Layer ? Forward/Backward Pass
• Regularization ? Dropout, Batch Normalization
• Data Preprocessing & Data Augmentation
• Weight Initialization Strategies
• Babysitting Learning: Overfit detection, TensorBoard Monitoring
• Hands-on: MLP on MNIST / Tabular data (e.g. HR Analytics)

Master CNNs, object detection, segmentation, and deployment.

• Basics of Images, Image Preprocessing
• Convolution: 2D Conv, Forward & Backward
• Pooling, Padding, Stride, Transposed Conv
• CNN Architectures: LeNet, AlexNet, VGG, ResNet
• GPU vs CPU for DL
• Transfer Learning: Inception, MobileNet, fine-tuning
• Semantic Segmentation using UNet
• Object Detection ? YOLO, SSD, Region Proposal
• Bounding Box Regressor
• Siamese Networks for Similarity Search
• Hands-on:
  • Image Classification with CNN
  • Object Detection with YOLOv8
  • Visual Search with Embeddings

Train text models from scratch and with BERT.

• Introduction to NLP and Use Cases
• Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
• Feature Extraction: BOW, TF-IDF, N-Grams
• Word Embeddings: Word2Vec, GloVe, Dense Vectors
• POS Tagging, Named Entity Recognition
• RNN, LSTM ? Forward Pass and BPTT
• Advanced LSTM Applications + Architectures
• Attention Mechanism + Encoder-Decoder
• Transformers, BERT, Hugging Face Pipelines
• NLP Evaluation Metrics: BLEU, ROUGE
• Hands-on:
  • Sentiment Classifier (LSTM or BERT)
  • Deploy NLP Model with Streamlit

Take models from notebooks to real-world applications.

• Saving & Loading Models (Pickle, Joblib, Keras)
• Flask vs FastAPI ? Serving ML models
• Streamlit/Gradio for Web Apps
• Hosting Models on Hugging Face Spaces, Streamlit Cloud
• MLflow Intro ? Model Tracking & Versioning
• Hands-on:
  • Deploy CV or NLP model with Streamlit
  • Create API using FastAPI

Build, evaluate, and deploy a mini AI project end-to-end.

• Project Selection: Tabular, CV, or NLP
• Data Collection/Exploration
• Preprocessing + Feature Engineering
• Model Training & Tuning
• Evaluation & Interpretation
• App Creation (Streamlit/Gradio)
• Deployment + Final Presentation/Submission