Data is the New Oil
Consumer behavior on social media and automated digital processes across business sectors are generating vast quantities of data 24x7. Powerful insights generated from this Big Data are a source of competitive advantage to the organizations who are able to invest in and use Data Science tools and technologies effectively. It is not surprising then to see that Data Science, Al, and ML skills are amongst the hottest disruptive skills in 2021 and beyond.
With the rapid proliferation of big data, companies are looking for data science professionals who are capable of uncovering business insights through data analysis and communicating those insights through succinct visualizations that help their companies make data-driven decisions.
Duration: As Little as 15 Weeks
Course Fee: As low as $8,500 USD
Most learners qualify for discounts or scholarships. Ask about options available to you!
Market Trends
- With the proliferation of the Internet of Things, the amount of data we generate continues to rise exponentially.
- Data will continue to almost double in size every two years if current trends continue.
- Consequently, jobs in big data and advanced analytics are in high demand.
Job Demand
Source: LinkedIn’s 2020 Emerging Jobs Report
Source: LinkedIn’s 2020 Emerging Jobs Report
Source: LinkedIn’s 2020 Emerging Jobs Report
Program Outline and Highlights
Data analysis using statistical techniques is a survival skill in data science. Initially, this program helps students build a strong data analysis foundation, with a special emphasis on using relevant statistical tools, and data visualization using various graphs, charts, and pivot tables in Excel. Students are then introduced to Python programming. They learn to use Python to write programs to do statistical analysis using libraries such as NumPy and Pandas.
Further on, they learn to query relational databases, to process and manage data using ETL processes, create data models and visualize data using Tableau. Data modeling and data-based storytelling are two key aspects by which big data is leveraged for decision-making. Overall, students become competent at data analysis, visualization, modeling and forecasting, and communicating and collaborating with all stakeholders.
Throughout their bootcamp, students will participate in up to three, two-week intensive preparation sessions for top in-demand certifications in Data Analytics, including Microsoft Certified Azure Fundamentals (AZ 900), Certified Tableau Data Analyst, Azure AI Fundamentals (AI-900) and Microsoft Certified Azure Data Scientist Associate (DP-100).
Student Guidelines
- Students must possess the curiosity and a determination to persist with demanding hands-on exercises and assignments.
- In addition, students need to fulfill the below
requirements:
- High School Diploma from an accredited institution
- Spoken and written English skills
- Appropriately configured PC with webcam and headset
- Uninterrupted internet connection
- Uninterrupted time to complete the learning activities on schedule
Delivery Guidelines
- Sessions will be conducted between 6:00PM – 10:00PM EST ON MONDAYS AND 6:00PM - 8:00PM EST ON THURSDAYS.
- Live online lectures on context-setting and concept building concepts
- 60% of the program is hands-on i.e. in each program, a student would spend over 60% of time on coding or hands-on activities
Who Should Attend?
Students who are keen on taking up a data analyst role or those looking for a career shift into big data analytics can take up this program. No prior programming or analytics experience is required to do this program - just curiosity and a determination to persist with the demanding hands-on exercises and assignments. Some other basic requirements are:
- High School Diploma from an accredited institution.
- Spoken and written English skills.
- Appropriately configured PC with webcam and headset.
- Uninterrupted internet connection.
- Uninterrupted time to complete the learning activities on schedule.
Exit Profile
This program delivers job-ready Data Science practitioners who can easily take up an entry-level role as a Data Analyst. Additionally, it sets them up for a future progression into the exciting new areas of Al and ML as Data Scientists or Data Engineers.
Our program gradually transforms students with no data analytics background into confident data analysts who can contribute effectively to data lifecycle activities such as data sourcing, data munging, wrangling and storage, data modeling and statistical analysis, data visualization, and data-based storytelling.
On successful completion of all the assignments and projects, each student will be able to:
- Analyze discrete data and structured data using Excel
- Apply descriptive and inferential statistical tools and techniques
- Summarize and represent data visually using graphs, charts, and pivot tables
- Create data dashboards using Excel
- Write Python programs using in-built data types, constructs, and standard libraries
- Use Pandas, NumPy for statistical analysis on large datasets
- Design and create data schemes for structured data
- Programmatically connect with RDBMS to retrieve, manipulate, and analyze data
- Slice and dice data to generate hypotheses
- Use statistical tools to validate a hypothesis
- Create advanced data dashboards & visualizations using Tableau
Program Coverage
Key Modules
- Data Analysis using Excel (Discrete and structured data, statistical tools, and techniques)
- Data Visualization using Excel (Data Visualization and Dashboarding)
- Python Programming (Solve problems using Python and its libraries)
- Data Analysis using Python (Pandas, NumPy, Intro to ML models)
- Data Processing and Management using RDBMS (SQL – DDL and DML to perform CRUD operations)
- Data Analysis using RDBMS and Python (Programmatically perform SQL queries, CRUD operations and “what-if” analysis)
- Data Processing and Management using ETL and Data Engineering
- Data Modelling (Data Analysis and Data Mining, Statistical Models)
- Data Visualization using Tableau
- Storytelling using Data
- Big Data Analytics (Classification, Clustering, and Regression, Social Media and Text Analysis)