Software Development

Foundations of Data Science and Artificial Intelligence with Python

Learn key concepts in data science and AI using Python for real-world problem solving.

Foundations of Data Science and Artificial Intelligence with Python
Track Software Development
Duration 30 hours
Skill Level Intermediate
Language English

About this Course

Learn key concepts in data science and AI using Python for real-world problem solving.
Learning Mode: Learn at ALC or at Home

Detailed Course Curriculum

Hands-on module breakdown aligned with MKCL production standards and industry requirements.

  • What is Data Science
  • Who is Data Scientist?
  • Why Data Science
  • Data Science Pipeline
  • Data Science Tools
  • Data Science Tools (Proprietary)
  • Data Science Tools (Proprietary)
  • Introduction to Python Tools for Data Science
  • Introduction to Python Tools for Data Science
  • Anaconda Installation and Setup
  • Anaconda Installation and Setup
  • Virtual Environment Setup with Anaconda
  • Virtual Environment Setup with Anaconda
  • What is PYPI?
  • What is PYPI?
  • Installing Packages via Pip
  • Installing Packages via Pip
  • Jupyter Notebook
  • What is a Jupyter Notebook
  • What is a Jupyter Notebook
  • Getting familiar with Jupyter Notebook
  • Getting familiar with Jupyter Notebook
  • Jupyter Magic Commands
  • Jupyter Magic Commands
  • Case Studies
  • Covid 19 Data Science Application
  • Covid 19 Data Science Application
  • JP Morgan
  • JP Morgan
  • Netflix User Case
  • Netflix User Case
  • UPS
  • UPS
  • Walmart
  • Walmart
  • Future of Data Scientist
  • Future of Data Scientist
  • Vector Introduction
  • Vector Arithmetic
  • Dot and cross product
  • Applications of Vectors
  • Probability Introduction
  • Conditional probability
  • Multiplication Rule of probability
  • Baye’s Theorem
  • Statistics Introduction
  • Discrete and continuous mathematics
  • Set Theory
  • Applications of set theory
  • Relations and Functions
  • Introduction to numpys
  • Creating numpy arrays and dimensions
  • Indexing
  • Numpy Slicing
  • Numpy Arithmetic Operations
  • Other Numpy Arithmetic Operations
  • Broadcasting and comparison
  • Solving equation with numpy
  • Statistical Operation with numpy
  • Numpy Exercises - Part 1
  • Numpy Exercises - Part 2
  • Create and manipulate arrays using numpy
  • Create and manipulate arrays using numpy
  • Combining 2 arrays
  • Combining 2 arrays
  • Compare the elements of the two arrays
  • Compare the elements of the two arrays
  • Program to print 2d diagonal array.
  • Program to print 2d diagonal array.
  • Flattening a 2d array
  • Flattening a 2d array
  • numpy.size() function
  • numpy.size() function
  • Non-Zero Functions with numpy
  • Non-Zero Functions with numpy
  • Changing Data Type
  • Changing Data Type
  • Trace of matrix
  • Trace of matrix
  • Addition of two matrix
  • Addition of two matrix
  • Subtraction of Two Matrix
  • Subtraction of Two Matrix
  • Intro video
  • Introduction to pandas
  • Data structures in pandas
  • Reading files in Csv
  • Retrieving data
  • Analysing data
  • Querying and sorting
  • Working with dates
  • Grouping and aggregation
  • Merging data from multiple sources
  • Writing data back to files
  • Basic Plotting with Pandas
  • Pandas Exercise
  • How to create a DataFrame from dict of arrays/lists
  • How to create a DataFrame from dict of arrays/lists
  • Creating Dataframe from lists
  • Creating Dataframe from lists
  • Creating Dataframe from a list of tuples
  • Creating Dataframe from a list of tuples
  • Create a list of nested dictionaries
  • Create a list of nested dictionaries
  • Pandas to create a dataframe
  • Pandas to create a dataframe
  • Displays the values of each row and column
  • Displays the values of each row and column
  • How to read data from a string with read_csv()
  • How to read data from a string with read_csv()
  • How to reindex rows using reindex() method
  • How to reindex rows using reindex() method
  • Create two pandas Series using NumPy linspace()
  • Create two pandas Series using NumPy linspace()
  • Preprocess Data
  • Intro video
  • Intro video
  • Why preprocess
  • Why preprocess
  • Preprocessing Technique
  • Preprocessing Technique
  • Null and NaN
  • Null and NaN
  • Forward Fill
  • Forward Fill
  • Selecting data with conditionals
  • Selecting data with conditionals
  • Dropping columns/rows
  • Dropping columns/rows
  • Subset and index data
  • Subset and index data
  • Reshaping
  • Reshaping
  • Pivoting
  • Pivoting
  • Rank and sort data
  • Rank and sort data
  • Matplotlib
  • Intro video
  • Intro video
  • Introduction to Matplotlib
  • Introduction to Matplotlib
  • Linchart
  • Linchart
  • Improving style using seaborn
  • Improving style using seaborn
  • Scatter plot
  • Scatter plot
  • Histogram
  • Histogram
  • BarChart
  • BarChart
  • HeatMap
  • HeatMap
  • Intro video
  • EDA Introduction
  • Data Preparation and Cleaning
  • Exploratory Analysis
  • Asking and answering questions
  • Intro video
  • What is Artificial Intelligence?
  • History of AI and its Development
  • Narrow or Weak AI
  • AI Techniques and Algorithms
  • Natural Language Processing
  • Ethical and Societal Implications
  • Relationship between AI and other Technologies
  • Robotics and its connection to AI
  • Difference between AI and ML
  • The Role of AI
  • Applications of AI
  • Use of AI in Social Media
  • Intro video
  • Numpy
  • Pandas
  • Matplotlib
  • SciKit-Learn
  • Tensorflow
  • Keras
  • PyTorch
  • The Natural Language Toolkit
  • XGBoost
  • CatBoost
  • OpenCV
  • Introduction to Machine Learning
  • Intro video
  • Intro video
  • Introduction of Machine Learning
  • Introduction of Machine Learning
  • Supervised Learning
  • Supervised Learning
  • Unsupervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Reinforcement Learning
  • Data Preprocessing
  • Data Preprocessing
  • Feature Extraction
  • Feature Extraction
  • Training data
  • Training data
  • Which model to use?
  • Which model to use?
  • Overfitting / Underfitting
  • Overfitting / Underfitting
  • Mathematical/Statistical Concepts for AI/ML
  • Intro video
  • Intro video
  • The necessity of Statistics for AI
  • The necessity of Statistics for AI
  • Vectors and Matrices
  • Vectors and Matrices
  • Graphs for AIML
  • Graphs for AIML
  • Sets for AIML
  • Sets for AIML
  • Probability distribution
  • Probability distribution
  • Hypothesis testing in AIML
  • Hypothesis testing in AIML
  • Markov model
  • Markov model
  • Clustering in AIML
  • Clustering in AIML
  • Kernel Functions in AIML
  • Kernel Functions in AIML
  • Decision Tree
  • Decision Tree
  • Intro video
  • Introduction to Supervised Learning
  • Classification
  • Regression
  • Naive Bayes
  • Linear Regression
  • Logistic Regression
  • Support Vector Machines (SVMs)
  • K Nearest Neighbor
  • Supervised Learning Applications
  • Challenges in Supervised Learning
  • Intro video
  • Introduction to Unsupervised Learning
  • Clustering in Unsupervised Learning
  • Exclusive and Overlapping Clustering
  • Hierarchical Clustering
  • Probabilistic Clustering
  • Association Rule
  • Dimensionality Reduction in Unsupervised Learning
  • Principal Component Analysis
  • Applications of Unsupervised Learning
  • Challenges in Unsupervised Learning
  • Intro video
  • What are Neural Networks
  • History
  • Types of Neural Network
  • Weights and Biases
  • How do Neural Networks Work? Part-1
  • How do Neural Networks Work? Part-2
  • Working of some common Neural Networks
  • Neural Network vs Deep Learning
  • Applications of Neural Network
  • Intro video
  • Define the problem and determine the goals of the model
  • Data preparations
  • Factors to consider while choosing model
  • Why to use CSV file?
  • Building the ML model (Logistic Regression) - P1
  • Building the ML model (Logistic Regression) - P2
  • Building the ML model (Logistic Regression) - P3
  • Building the ML model (Logistic Regression) - P4
  • Building the ML model (Logistic Regression) - P5
  • Building the ML model (Logistic Regression) - P6
  • Building the ML model (Logistic Regression) - P7
  • Building the ML model (Logistic Regression) - P8
  • Building the ML model (Logistic Regression) - P9
  • Building the ML model (Logistic Regression) - P10
  • Building the ML model (Logistic Regression) - P11
  • Intro video
  • Importance to evaluate the ML model
  • Accuracy of ML model
  • Precision measure of the ML model
  • Recall
  • F1 Score
  • Confusion Matrix
  • Techniques to improve accuracy
  • Summary of the course
Eligibility Criteria
• Basic knowledge of computers and keen desire to build skills in this field.
• Open to students, job seekers, and working professionals.
Official Certification
• Official MKCL KLiC Certificate upon successful completion of the course and evaluations.
Work-Centric Learning Approach
• Step 1: Learners are given an overview of the course and its connection to life and work
• Step 2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
• Step 3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
• Step 4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
• Step 5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
• Step 6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
• Step 7: Learners are engaged in appreciation of real-life case studies developed by the experts.
• Step 8: Learners are encouraged to proceed from appreciation to imitation of the experts.
• Step 9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
• Step 10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
• Step 11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
• Step 12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!

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Course Overview
  • Duration: 30 hours
  • Track: Software Development
  • Skill Level: Intermediate
  • Language: English
  • Mode: Learn at ALC or at Home
  • Certificate: Official MKCL
Key Course Highlights:
  • By the end of the course, learners will be able to:
  • Build proficiency in data science concepts, tools, and applications.
  • Effectively utilize tools like Anaconda, Jupyter Notebooks, and PyPI in real-world data science projects.
  • Make use of mathematical concepts in practical data science scenarios, enhancing analytical skills.
  • Construct efficient numerical operations and data manipulation tasks using NumPy.
  • Examine and Manipulate data effectively using Pandas for insightful decision-making.
  • Build data preprocessing techniques to handle null values, reshape data, and perform conditional selections.
  • Produce clear and meaningful visualizations using Matplotlib for effective exploratory data analysis.
  • Compare real-world case studies and apply data science techniques to address complex business challenges.
  • Distinguish the foundations of artificial intelligence and its ethical implications in societal contexts.
  • Develop a solid understanding of machine learning fundamentals, preparing for advanced applications and scenarios.
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