PGP in Data Science and AI
- Certification Courses, IT Courses
- 803 (Registered)
03
Oct
Welcome aboard SkillSchool’s practical-based program, designed to train future data science experts and AI engineers. Students will deep dive into the intricate world of data and the systematic analytical approaches that power practically every industry today. The program will equip you with the foundational knowledge of data science and AI, and also give you the right impetus for career advancement. Gain a deep understanding of data science and AI and their expansive application in the digital era, taught by distinguished faculty with years of industry experience.
Duration: 9 Months
Blend of Theory and Practice: 6 Months of Course Content engagement and 3 months of Capstones, Practical learning, Soft Skills and Interview Preparation
6 Months focus on Course Module and 3 Months of Capstones, Soft skills training, and interview preparation
Beginner-Friendly Level: Fresh Graduates can also join
Industry-experienced trainers: Learn from the best with extensive experience
Industry-Based Curriculum: Course modules tailored to the Data Science industry by incorporating the latest trends
Modules with key topics: Learn the fundamentals of Data Science and AI
Student Support: 24/7 Assistance
Soft Skills Training: Overall skill development
Job Preparation Support: Mock Interviews, Resume writing, etc
Program Overview
The PGP in Data Science and AI at SkillSchool prepares you for the most in-demand jobs by building competencies that will make you stand out in the competition. Individuals with the knack and passion for unraveling the power of data and exploring the fascinating world of AI will find this program a great head start. Students can expect to learn in-depth about trending areas of Data Science and AI, including Big Data, Data visualization, Machine Learning, and Deep Learning, to mention a few.
SkillSchool designed this program curriculum by incorporating the industry's latest trends to ensure our students meet the requirements for fulfilling their career aspirations. Our strong team of faculty with an established reputation in the industry for their knowledge will take you on an adventurous and stimulating learning experience. Students will develop a strong theoretical foundation and the prowess to develop practical solutions for society. The practical section of the program and the exposure to real-world technological instances will strengthen what they learned from the course.
SkillSchool's PGP in Data Science and AI is an excellent pathway for aspirants to master the nuances of data science and AI and move further into specialized expertise. If you are seeking to optimize your career prospects or become a Data Science and AI expert today, join the PGP in Data Science and AI today and earn a competitive edge.
Course Content
Postgraduate Program in Data Science and AI
Module 1: Python Fundamentals: Introduction to Python
- Introduction to Python
- Python applications in data science and AI
- Python basics: data types, variables, operators, and control flow
- Working with data structures: lists, tuples, dictionaries, and sets
- Functions and modules in Python File handling and data input/output
- Object-oriented programming in Python
Module 2: Getting Started with MySQL for Data Science
- Introduction to relational databases and MYSQL
- Creating databases, tables and relationships between DDL and DML commands in MYSQL
- SQL queries: Exploring several operations, including where, having, groupby, limit, order by, operators, wildcards, etc. Managing data with INSERT, UPDATE, DELETE
- Joins: Inner, Left, Right, Full-Outer Join, Cross Join Subqueries and Views
- Connecting MySQL with Python DDL commands with Python DML commands with Python
Module 3: Data Analysis using Python
- NumPy Library
- Creating and Manipulating Multi-Dimensional Arrays
- NumPy Arithmetic Functions
- Concatenation and Stacking of Arrays
- Eyes, Ones, Zeros arrays
- Random Array Generation
- Data analysis: overview and its importance in decision-making
- Data preprocessing: data cleaning, handling missing values, and data transformation
- Introduction to Pandas
- Data Manipulation & Analysis
- Data visualization using libraries like Matplotlib and Seaborn Line Chart, Bar Chart, Stacke- Bar Chart, Histograms, Scatter Plot, Pie Chart, Box Plot, Pairplot, Heatmaps, Subplots, etc.
- Exploratory Data Analysis (EDA) techniques
- Performing EDA on an Industrial Dataset
Module 4: Statistics in Machine Learning
- Understanding Statistics for Machine Learning
- Definition of statistics
- Importance of statistics in machine learning
- Dissecting the role of statistics in data-driven decision-making
- Common Statistical concepts used in ML
Introduction to descriptive statistics and its purpose:
- Measures of central tendency: mean, median, mode
- Measures of variability: range, variance, standard deviation
- Data visualization techniques: histograms, box plots, and scatter plots
Probability Basics :
- Probability rules: addition and multiplication rules
- Applications of probability in machine learning algorithms
Statistical Distributions:
- Normal & Standard Distribution and its properties
- Binomial distribution and its applications in binary classification
- Poisson distribution and its use in count data analysis
Hypothesis Testing
- Understanding hypothesis testing and its role in machine learning
- Null and alternative hypotheses
- Performing a hypothesis test: z-test and t-test
- P-values significance
Module 5: Machine Learning
- Introduction to Machine Learning and Linear Regression
- Introduction to Machine Learning and its Applications
- Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
- Data preprocessing and feature engineering
- Linear Regression: Theory and Implementation in Python
- Model evaluation and metrics for regression
Regularization and Logistic Regression
- Regularization techniques: Lasso and Ridge Regression
- Logistic Regression: Theory and Implementation in Python Model evaluation and metrics for classification
KNN & Naïve Bayes
- K-Nearest Neighbours (KNN): Theory and Implementation in Python Model Evaluation
- Naïve Bayes: Theory and Implementation in Python Model Evaluation
Decision Trees & Random Forests
- Decision Trees: Theory & Implementation in Python
- Parameter Tuning of Decision Trees
- Cross Validation Technique
- Ensemble Methods: Random Forests
- Parameter Tuning of Random Forests
Support Vector Machines (SVMs)
- Handling imbalanced data in classification
- Scaling of Data
- Introduction to Support Vector Machines (SVM): Theory
- SVM implementation in Python with Scikit-learn
- Kernel methods and non-linear SVM
Boosting
- Understanding Boosting
- Boosting vs Bagging
- AdaBoost Technique
- Understanding Gradient Boosting Concept
- Implementation of Gradient Boosting
Introduction to Unsupervised Learning
- Use cases of Unsupervised Learning
- K-Means Clustering: Theory and Implementation in Python
- Elbow Curve
- Silhouette Score
- Hierarchical Clustering: Theory and Implementation in Python
- Agglomerative Clustering
- Divisive Clustering
Dimensionality Reduction
- Understanding the Curse of Dimensionality
- PCA: Principal Component Analysis Theory
- Eigenvalues and Eigen Vectors
- Implementation of PCA using Python
- N-Components
- Variance, Variance Ratio
Hyperparameter Tuning
- Improving the performance of ML Models
- Finding the best-performing models
- Randomized Search CV
- GridSearch CV
- ML Pipelining
Module 6: Artificial Intelligence and NLP
- Introduction to Artificial Intelligence (AI)
- What is Artificial Intelligence (AI) and its applications
- Overview of AI history and key milestones
- Understanding AI subfields: Robotics, Computer Vision, Natural Language Processing (NLP), and more
- Activation functions
- Multi-layer Perceptrons and feedforward neural networks
Fundamentals of Deep Learning – I
- Deep Learning and its divergence from traditional programming
- Building Blocks of Neurons, Layers, and Activation Functions
- Neural networks and their structure
- Hands-on: Introduction to AI tools and platforms for beginners TensorFlow, Keras, PyTorch, scikit-learn, etc.
Fundamentals of Deep Learning – II
- Convolutional Neural Networks (CNNs) for Image Classification
- Recurrent Neural Networks (RNNs) for Sequence Data
- Introduction to TensorFlow and Keras in Deep Learning Hands-on: Building a simple neural network for image recognition
Natural Language Processing (NLP)
- Understanding NLP and its applications in language understanding Tokenization, stemming, and lemmatization
- Text representation methods: Bag-of-Words and word embeddings
- Introduction to NLTK libraries for NLP in Python
- Hands-on: Building a basic chatbot using NLP techniques
AI in Practice and Future Trends
- Real-world AI applications and success stories
- Ethical considerations and challenges in AI development
- Future trends in AI, DL, and NLP
- Exploring Chat-GPT and Google Bard
Module 7: Advance Analytics using Power BI
Power BI for Data Analysis
- Introduction to Power BI and its capabilities
- Power BI Products: Power BI Desktop and Power BI Service
- Navigating the Power BI interface
- Connecting to various data sources and importing data
- Data transformation and modeling in Power Query Editor
- Cleaning and Shaping Data for Analysis
- Creating interactive visualizations: charts, graphs, and maps
Module 8:Capstone Project
The capstone project is the final part of the program and is a mandatory section for all students to put their skills into action. Our trainers will guide you in solving real-world, industry-relevant Data Science problems. Students will get to practically work with data processing and model building and reporting the results, findings and insights from the project.
Become a Certified Data Science Professional
Live Online Program
Learn Data Science and AI through an interactive learning experience from anywhere and build a solid career foundation.
Blending Theory and Practice
Engage in overall growth with 6 months of theoretical knowledge building and 3 months of practical competencies, preparing you to excel in the job market.
Industry-Specific Curriculum
Meticulously designed curriculum, developed in collaboration with industry experts to prepare you with industry-relevant and in-demand skills.
Expert and Qualified Faculty
Learn from the experts and train under senior data science professionals with industry-established reputations and years of experience in Data and AI.
Certification
Earn certification to add more value to your profile and stand out in the competition.
Dedicated Mentorship
Receive personalized guidance and support from expert mentors who will help streamline your learning experience to your career goals.
Round-the-clock support
24/7 support service and assistance for any program-related queries and guidance.
Job-ready Prep
Make the most of the practical job preparation sessions, from soft skill development to resume building to mock interviews.
Learning Management System (LMS) Support
Enjoy round-the-clock access to our Learning Management System, which supports your educational journey with additional resources.
Candidates applying to the SkillSchool PGP in Data Science and AI must fulfil the following program requirements to be enrolled:
- Bachelor’s Degree from a recognized institution/university with a minimum of 50% in Computer Science, Statistics, Mathematics or any relevant field with Mathematics as a major subject.
- Desirable skills (not mandatory): Basic Knowledge of Programming language.
- Any Interested Individual: Seeking data science and AI skills to build a career in tech-driven fields
- Fresh Graduates: Desiring to build a career in Data Science and AI fields
- Data Science/Working Professionals: Seeking to enhance data science and AI skills
- Career Switchers: Professionals desiring to switch their profession to data science and AI-related fields.
Program Outcomes
Solid Foundation
Gain a solid grounding in the fundamentals of data science, AI, ML and Deep learning.
Data-driven business acumen
Capable of driving business growth with data-informed insights
Conversant with tools
Adept at using analytical tools and technologies like Python, MySQL, Power BI, etc.
Adept at DL and NLP
Gain proficiency in Natural Language Processing (NLP) and Deep Learning
Proficient at Data-driven Organizational tasks
Ability to integrate data-informed insights and decisions across organizations or businesses
Analytical Ability
Adept at using statistics and data modelling to structure business problems into an analytical framework
Become Job Ready
Be ready to compete in the job market for various positions like Data Scientist, AI engineer, AI Consultant, Data Analyst, etc.
Program FAQS
What is the duration of the PGP in Data Science and AI course?
The PGP in Data Science and AI at SkillSchool will run for 9 months, with a properly structured schedule for theory and practice. The first 6 months will be engaged in learning the theoretical concepts and fundamentals and the remaining 3 months will be dedicated to soft skill building, including Capstone projects, interview mock tests and job preparation.
Who is the SkillSchool PGP in Data Science and AI program for?
The PGP in Data Science and AI at SkillSchool is open to the following candidates:
- Any Interested Individual
- Fresh Graduates
- Data Science professional
- Working Professionals
- Career Switchers
Will I get a certificate if I complete the Program?
Yes. Skillschool will issue a certificate to every student who successfully completes the PGP in Data Science and AI. The certificate is powered by ICASR upon successful completion.
What are the eligibility criteria to be enrolled in the program?
Students seeking enrollment in the program must fulfil the following requirements:
Bachelor’s Degree from a recognized institution/university with a minimum of 50% in Computer Science, Statistics, Mathematics or any relevant field with Mathematics as a major subject.
- Basic computer knowledge.
- Familiarity with programming languages is desirable
- Knowledge of fundamental mathematics
What career opportunities can I seek after the PGP in Data Science and AI?
Data Science and AI skills are in high demand across industries today, which reflects an excellent career opportunity for students who pursue this career path.
Here are the most popular career tracks that you can seek after the completion of PGP in Data Science and AI:
- Data Analytics Consultant
- Manager Analytics
- Data Analyst
- Data Scientist
- Data Engineer
- Machine Learning Engineer
- AI Engineer
- Reporting Analyst
- Research Analyst
- Research Executive
- Statistician
- Database Administrator
- Data Architect
Can I join the PGP in Data Science and AI from any location?
Skillschool is driven by the goal of enabling every potential youth in the country access to quality training and education to realize their dreams. All the courses we provide are completely online, allowing learners from any part of the country to enrol in the program that they wish. You can definitely be enrolled at the PGP in Data Science and AI from your location.
What technical equipment/devices do I need for the course?
Students must ensure having a stable internet connection and a personal device (laptop/tablet/desktop) to sit for the online classes and to access the student’s LMS.
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