Before the COVID-19 pandemic started in 2020, there were around 3 million certified data science professionals and data analyst trainers. By 2021, this number had tripled to 8.9 million, of which a majority of the analysts were certified in India and China alone! This number could rise to over 20 million by end of 2025, a significant boost considering that the demand for top quality talent is always there, and the average number of data scientists in top tier organizations ranges between 25 and 45. If you enquire about a data science course near you, you would be astonished to find that a majority of these courses are sold out, and the classroom sessions are pre-booked weeks in advance. That’s the kind of rush we are seeing in the data science industry. So, what kind of skills are these data science online courses teaching to the students?
According to a recent survey, a large number of students (nearly 66%) that enroll in an online course are either beginners with basic graduation or are software engineers with little or no experience in data science. This means a majority of the consultants and career counselors are forced to deal with questions related to what kind of skills are taught in the top data science course near me and you. In this article, we help identify these basic skills that data science students are taught in online and live training centres.
Excel for Data Science
Excel basics for data science could prove to be a very important step in your analyst career. Proficiency in Excel programming and analytics can equip me with the master skills required for data wrangling and mining, which invariably set up the path for data ecosystems. Students who master Excel training with Python and R invariably complete the data science certification in less than a year, even if it means adding specific Excel analytics skills for data manipulation, SQL, Python, Jupyter, and database architecture management.
Data Management and Big Data
It is impossible to complete the data science course without demonstrating skills in big data management and analysis. 60 percent of the course curriculum would revolve around data science applications pertaining to big data management. Extensive training with Tableau, SAS, MongoDB, Spark, and Python prepare participants to showcase their analytical skills in recognized big data projects approved by industry experts. Big Data skills that are taught in the top data science courses include Statistical Analysis, Hadoop, Spark, Machine Learning, Scala, and real-time streaming/ batch streaming.
Since big data analytics is a booming science, you will find many different curriculums and courses in the market. These prepare professionals to develop data driven Machine Learning and AI solutions for different kinds of projects.
Big data skills can be acquired in 90 days, and these would set up your next level of training in AI ML development or business intelligence, depending on your choice.
Commonly, big data analysts prefer to work in these ecosystems after completion of their training program:
- AWS Lambda
- Apache Spark
- IBM Red Hat
- Containers and Kubernetes, and so on
Data science and cloud computing/ computational programming have become synonymous with each other. Heroku, AWS, Microsoft Azure, Google Cloud Platform (GCPs), and others are hiring data analysts to dynamically transform and continuously improve their cloud service and user experience.
Cloud programming skills taught in the data science courses could include:
- Python based Linear Regression
- Clustering in Open Stack or GitHub
- APIs management to integrate cloud workloads
- Batch streaming
- ETL / ELT
- REST / GraphQL
- Multi cloud deployment, hybrid cloud networking, and DevOps
- AI operations and optimization
How much time does it take to master data science basics and advanced?
Basics: 4 weeks
Advanced: 6 weeks
- Conditional formatting and data validation
- Tableau Excel Dashboards
- Statistical Analysis
- Linear Algebra
- Machine Learning based Business Analytics and Business intelligence
- Data Management and Big Data
Additional training in Python for DevOps and Cloud, Django, Ruby, and GitHub could prove to be very useful when you develop new applications using Scala, Go, REACT, and Java/ C++ with advanced training in database management.
Cloud integration, managed services providers (MSPs), security, and demand surge are some of the new concepts that focus on how fast Infrastructure as a Service (IAAS) providers and Data as a Service provider (DAAS) meet customer requirements.