The Data Science Training Program is a comprehensive training program to help anyone interested in pursuing a career in AI, Machine Learning, and Data Science develop career-relevant skills and expertise.
Data science is one of the hottest professions of the decade, and the demand for professionals skilled in data science has never been greater. Entering the world of Data Science requires an amalgam of experience, Data Science knowledge, and using the correct tools and technologies. It is a solid career choice for both new and experienced professionals. TechClass Data Science training program is a comprehensive online degree program tailored for AI and data science-related job positions to prepare the students for the trending job opportunities in the industry.
Data Science Training Program's learning path includes three options with a vast portfolio of practical courses. It will provide the students with a step-by-step guide towards learning the latest job-ready skills.
Learning outcomes
Data Manipulation and Understanding
Learn the most common practices for statistical data analysis in Python. Gain expertise in using SQL and database management systems to manage, analyze, and query data and extract insights. Master the practical approaches to data manipulation and exploratory data analysis using popular Python packages such as Pandas. Gain expertise in mathematical computing using the Numpy package.
Data Visualization
Become proficient in transforming results and information into graphical representations. Gain expertise in creating insightful visualizations using Matplotlib and Seaborn libraries. Learn to analyze data and create interactive visualizations, reports, and dashboards using Tableau and Microsoft Power BI.
Predictive Modeling and Evaluation
Obtain a comprehensive knowledge of supervised and unsupervised learning models such as linear regression, logistic regression, SVM, decision trees, K-NN, neural networks, clustering, dimensionality reduction, etc., and implement them using the Scikit-learn library. Understand Deep Learning techniques applied in Computer Vision, and train several powerful Deep Learning models using TensorFlow and PyTorch.
Model Building and Deployment
Understand the components of enterprise-grade Machine Learning and gain hands-on experience with Azure Machine Learning service and AWS Machine Learning for cloud-based implementations. Learn how to prepare and deploy Machine Learning solutions to production.
Options
It is essential to understand each students' needs and provide a training package suitable for such demands. Students have three options to pick when they register in this program. Some want only to get familiar with the fundamental topics, some want to have more comprehensive knowledge, and others may wish to obtain ultimate and extensive skills and expertise.
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Fast-track (60 credits)
Mandatory: 40 credits / Elective: 10 credits / Project : 5 credits / Job Preparation: 5 credits
This option would be a suitable start to familiarize the students with the most fundamental topics. Throughout this program, students can make sure the area is interesting for them and obtain skills for entry-level jobs in the field.
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Professional (90 credits)
Mandatory: 40 credits / Elective: 35 credits / Project : 10 credits / Job Preparation: 5 credits
This option will start with the fundamental topics, then allow the students to select more elective subjects and extend their skills. The plan is to prepare the students for mid-level positions throughout this option.
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Masters (120 credits)
Mandatory: 40 credits / Elective: 60 credits / Project : 15 credits / Job Preparation: 5 credits
This option enables the students to have extensive and comprehensive skills and practical knowledge in the program's scope. Students may select various topics from the elective portfolio and even learn extended material.
Learning path
Stage 1: Introduction
- Introduction to the program (0 credits)
Stage 2: Mandatory
- Introduction to Python for Data Science (10 credits)
- Exploratory Data Analysis with Python (10 credits)
- SQL for Data Science (5 credits)
- Fundamentals of Machine Learning (5 credits)
- Machine Learning with Python (10 credits)
Stage 3: Elective
- Mathematics and Statistics Essentials (5 credits)
- Programming Refresher (5 credits)
- Basic SQL in PostgreSQL (5 credits)
- Business Analytics with Power BI (15 credits)
- Business Analytics with Tableau (15 credits)
- Fundamentals of AI (5 credits)
- Fundamentals of Reinforcement Learning (5 credits)
- Ethics of AI (5 credits)
- Fundamentals of Deep Learning (5 credits)
- Applied Deep Learning with TensorFlow (10 credits)
- Applied Deep Learning with PyTorch (10 credits)
- Introduction to Azure Cognitive Services (5 credits)
- Azure Cognitive Services: Speech (10 credits)
- Azure Cognitive Services: Language (10 credits)
- Azure Cognitive Services: Vision (10 credits)
- Azure Cognitive Services: Decision (10 credits)
- Introduction to Natural Language Processing (5 credits
- Applied Machine Learning with NLP (10 credits)
- Applied Machine Learning with Time Series Forecasting (10 credits)
- Access Web Data with Python (5 credits)
- Introduction to Computer Vision and Image Processing (10 credits)
- Cloud Machine Learning with AWS (15 credits)
- Cloud Machine Learning with Azure (15 credits)
Stage 4: Project
- Final Project (5/10/15 credits)*
*Based on the program option. Fast-track: 5 credits / Professional: 10 credits / Masters: 15 credits.
Stage 5: Job Preparation
- Job Preparation (5 credits)
Brochure
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