uLektz Skills

Learning Path

Non-Linear: Random Order

About the Course

Course Objectives

Understanding Core Concepts: Grasp the fundamentals of data science and machine learning techniques.
Proficient Use of Tools: Gain hands-on experience with data science and machine learning tools such as Python, R, TensorFlow, and scikit-learn.
Application Development: Learn to develop and implement data-driven applications and machine learning models.
Integration with Other Technologies: Understand how data science and machine learning integrate with big data, cloud computing, and AI.

Learning Objectives

Data Manipulation: Develop skills to clean, preprocess, and manipulate data.
Statistical Analysis: Learn to apply statistical methods to analyze data and draw meaningful conclusions.
Machine Learning Algorithms: Gain proficiency in implementing and tuning various machine learning algorithms.
Data Visualization: Learn to create insightful visualizations to communicate data findings effectively.
Model Evaluation: Understand how to evaluate and improve machine learning models using various metrics and techniques.

Skills Development

Technical Proficiency: Mastery of programming languages like Python and R, and tools like TensorFlow, Keras, and scikit-learn.
Analytical Skills: Develop the ability to analyze and interpret complex data sets.
Problem-Solving: Enhance problem-solving skills by tackling real-world data challenges.
Communication: Learn to communicate data insights effectively to stakeholders through visualizations and reports.
Research Skills: Develop the ability to stay updated with the latest trends and advancements in data science and machine learning.

Career Path

Data Scientist: Analyze large datasets to extract insights and build predictive models.
Machine Learning Engineer: Develop and deploy machine learning models and algorithms.
Data Analyst: Interpret data and provide actionable insights to guide business decisions.
Business Intelligence Analyst: Create reports and dashboards to help organizations make data-driven decisions.
AI Research Scientist: Conduct research and develop new algorithms and models in the field of artificial intelligence.
Big Data Engineer: Design and manage large-scale data processing systems.
Data Engineer: Build and maintain data pipelines to ensure data is accessible and usable for analysis.

Course Study Materials

Introduction to Data Science and Machine Learning

What is Data Science
Overview of Machine Learning and AI
The Data Science Workflow
Applications of Data Science and Machine Learning
Ethical Considerations in AI and Data Usage
Unit 1-Self Assessment 20 Questions

Data Collection and Preprocessing

Types of Data Structured, Semi-Structured, and Unstructured
Data Collection Techniques
Handling Missing and Outlier Data
Data Transformation and Feature Scaling
Exploratory Data Analysis
Unit 2-Self Assessment 20 Questions

Programming for Data Science

Introduction to Python and R for Data Science
Libraries for Data Analysis
Data Visualization Tools
Working with Databases and SQL
Jupyter Notebooks for Interactive Development
Unit 3-Self Assessment 20 Questions

Statistical Foundations

Descriptive and Inferential Statistics
Descriptive and Inferential Statistics.
Correlation and Regression Analysis
Dimensionality Reduction Techniques
Sampling and Data Partitioning
Unit 4-Self Assessment 20 Questions

Machine Learning Basics

Supervised vs. Unsupervised Learning
Common Algorithms
Evaluation Metrics
Cross-Validation and Hyperparameter Tuning
Avoiding Overfitting and Underfitting
Unit 5-Self Assessment 20 Questions

Advanced Machine Learning Techniques

Decision Trees and Random Forests
Gradient Boosting Techniques
Clustering Algorithms
Recommender Systems
Ensemble Learning Methods
Unit 6-Self Assessment 20 Questions

Natural Language Processing (NLP)

Basics of Text Preprocessing
Bag of Words and TF-IDF
Sentiment Analysis and Text Classification
Word Embeddings
Introduction to Transformers and BERT
Unit 7-Self Assessment 20 Questions

Deep Learning Fundamentals

Introduction to Neural Networks
Activation Functions and Loss Functions
Convolutional Neural Networks (CNNs) for Image Processing
Recurrent Neural Networks (RNNs) and LSTMs for Sequential Data
Transfer Learning and Pretrained Models
Unit 8-Self Assessment 20 Questions

Data Visualization and Communication

Principles of Effective Data Visualization
Tools for Interactive Dashboards
Visualizing Machine Learning Models
Storytelling with Data
Case Studies in Data Communication
Unit 9-Self Assessment 20 Questions

Big Data and Scalable Machine Learning

Introduction to Big Data Frameworks
Distributed Computing for Large Datasets
Working with Cloud Platforms
Scalable Machine Learning with Spark MLlib
Case Studies of Big Data Applications
Unit 10-Self Assessment 20 Questions

Industry Applications and Domains

Machine Learning in Finance
Healthcare Applications
Retail and Marketing Analytics
Natural Sciences
Emerging Domains in AI
Unit 11-Self Assessment 20 Questions

Capstone Project and Case Studies

Real-World Data Science Problem Solving
Developing and Deploying a Machine Learning Model
Analysis of Industry-Specific Case Studies
Peer Review and Feedback on Capstone Projects
Reflective Learning from Capstone Challenges
Unit 12-Self Assessment 20 Questions

The certificate issued for the Course will have

Student's Name
Photograph
Course Title
Certificate Number
Date of Course Completion
Name(s) and Logo(s) of the Certifying Bodies

Only the e-certificate will be made available. No Hard copies. The certificates issued by The Academic Council of uLektz. can be e-verifiable at www.ulektzskills.com/verify.

Students will be assessed both at the end of each module and at the end of the Course.
Students scoring a minimum of 50% in the assessments are considered for Certifications