Data Analytics Training Program is a comprehensive training program to help anyone interested in pursuing a career in the Data Analytics industry develop career-relevant skills and expertise.
Data analytics helps organizations make sense of data to find trends and draw conclusions. Due to the significant growth of the data analytics market, the demand for professionals skilled in data analytics has never been greater. Entering the world of data analytics requires an amalgam of experience, data analytics knowledge, and using the correct tools and technologies. It is a solid career choice for both new and experienced professionals. TechClass Data Analytics training program is a comprehensive online degree program tailored for data science and analytics-related job positions to prepare the students for the trending job opportunities in the industry.
Data Analytics Training Program 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
Learn the most common practices of data ingestion, cleaning, and manipulation. Gain expertise in using relational (SQL) and non-relational databases (NoSQL) to manage, query, and filter data and extract information. Learn how to ingest and integrate data from different data sources. Master the practical approaches to data cleaning and manipulation using Excel and popular Python packages such as Pandas, NumPy, and Scikit-learn.
Data Analysis
Master the essential skills of data analysis using Python and Excel. Gain hands-on experience using Python for data inspection, exploratory data analysis, and statistical data analysis. Gain expertise in mathematical computing, data transformation, basic data modeling using popular Python libraries such as Pandas, NumPy, Scipy, and Scikit-learn. Learn how to use Excel to transform data and perform calculations.
Data Understanding
Learn how to use different analytics methodologies to extract knowledge and insights from data. Gain expertise in using database management systems to analyze data and extract information. Master the practical skills of statistical data analysis and modeling using Python to find trends in data and extract meaningful insights. Learn how to apply various operations to data to understand the data better.
Data Visualization
Become proficient in transforming results and information into graphical representations. Gain expertise in creating insightful visualizations using Matplotlib and Seaborn libraries. Gain hands-on experience visualizing and presenting data and analysis results for insight extraction and storytelling. Learn to analyze data and create interactive visualizations, reports, and dashboards using Tableau and Microsoft Power BI.
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
- SQL for Data Science (5 credits)
- MongoDB and NoSQL Databases (5 credits)
- Excel (5 credits)
- Statistical Data Analysis with Python (10 credits)
- Business Analytics (Microsoft Power BI or Tableau) (15 credits)
Stage 3: Elective
- Mathematics and Statistics Essentials (5 credits)
- Programming Refresher (5 credits)
- Basic SQL in PostgreSQL (5 credits)
- Introduction to Python for Data Science (10 credits)
- Exploratory Data Analysis with Python (10 credits)
- Machine Learning with Python (10 credits)
- Predictive Modeling and Analytics with Python (10 credits)
- Access Web Data with Python (5 credits)
- Storytelling Through Data Visualization (5 credits)
- Data Analytics on Cloud with AWS (15 credits)
- Data Analytics on Cloud 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
