Analytics is defined as the scientific process of transforming data into insight for making better decisions. It helps improve processes, saves cost and enhances revenue.
Data Analyst is one of the key entry-level roles which fresh graduates should consider while planning to start a career in Analytics.
WHAT DO DATA ANALYSTS DO?
Data analysts translate numbers into plain English Every business collects data, whether it's sales figures, market research, logistics, or transportation costs. A data analyst's job is to take that data and use it to help companies make better business decisions. This could mean figuring out how to price new materials for the market, how to reduce transportation costs, solve issues that cost the company money, or determine how many people should be working on Saturdays.
There are many different types of data analysts in the field, including operations analysts, marketing analysts, financial analysts, etc
WHAT ARE THE EDUCATION REQUIREMENTS?
A bachelor's degree is needed for most entry-level jobs, and a master's degree will be needed for many upper-level jobs. Most analysts will have degrees in fields like math, statistics, computer science, or something closely related to their field. Strong math and analysis skills are needed.
Depending on the field you go into, certification is available.
HOW MUCH DO DATA ANALYSTS MAKE?
This depends on experience and what type of data analyst you are. According to PayScale, an entry-level Data Analyts earn $55,275 per annum on average.
To get more insights on role progression from Data Analyst, create your career path on MyTechlogy.
WHAT ARE THE TECHNICAL SKILLS REQUIRED?
To become a Data analyst you should try to acquire a certain set of skills which would really help to learn and work in Data Science. I have years of experience in Data Science with a vast teaching experience and I would just like to give an overview of few of the skills that I think are very important.
- Math (e.g. linear algebra, calculus and probability)
- Statistics (e.g. hypothesis testing and summary statistics)
- R and/or SAS languages
- SQL databases and database querying languages
- Python (most common), C/C++ Java, Perl
- Machine learning tools and techniques (e.g. k-nearest neighbors, random forests, ensemble methods, etc.)
- This list is always subject to change. I believe generic programming skills are a lot more important than being the expert of any particular programming language.
WHAT ARE THE BUSINESS SKILLS REQUIRED?
Analytic Problem-Solving: Approaching high-level challenges with a clear eye on what is important; employing the right approach/methods to make the maximum use of time and human resources.
Effective Communication: Detailing your techniques and discoveries to technical and non-technical audiences in a language they can understand.
HOW TO ACQUIRE THE SKILL-SET AS A FRESH GRADUATE?
You can learn Data Science online with the various courses that are offered but I believe a fresher should always go for a blended course which gives classroom training to acquire the right knowledge, industry exposure to learn the skills and build a network and online practical learning.
Pick up a project on Kaggle or Hackerearth and start learning R, Python or any other tool. You'll start learning things gradually. Start from small eventually you'll end up big. All the best. Make a portfolio of projects that you do with the code, visualizations, analysis in words, conclusions, thoughts and everything around that project.
Here are Some of the courses that can help you master the concepts required for getting into Data Analytics
- Statistics for Business Analytics A-Z™ (Rated 4.4/5 by 1455 students)
- Statistics for Data Science and Business Analysis (Rated 4.5/5 by 647 students)
- Business Statistics and Analysis Specialization
- Learning Python for Data Analysis and Visualization
- Python A-Z™: Python For Data Science With Real Exercises!
- Getting Started with Data Analysis Using Python (Rated 3.5/5 by 20 Students)
- Introduction to Data Science in Python
- Python Data Analysis
- Data Processing Using Python
- Complete & Practical SAS, Statistics & Data Analysis Course (Rated 4.1/5 by 605 students)
- Learn SAS And Become A Data Ninja (Rated 4.3/5 by 290 students)
- Data Analysis and Interpretation Specialization
Now to ensure that your resume grabs eyeballs when you apply to an analytics firm needs some preparation. The preparation would be different for a fresher than for someone who already has some work experience under his belt albeit in a different domain. For a fresher generally engineering or maths/ stats graduates the focus is more on analytical problem solving and exposure to some programming language and then they can apply to analytics firms for jobs.
CAREER ADVICE FROM DATA SCIENCE EXPERTS
There is no one size fits all solution. Your situation could be different (educational background, experience etc.), this is where experts and mentors from Data Science field understand your profile better and guide you in the right path.
Like what Suzy Welch says "Your mentor doesn't need to have seniority over you." A mentor just has to do something better than you do.
Here are some of the experts who are willing to help you achieve your career goals. They conduct a personal one–to–one over Skype and discuss your profile and areas for improvement.
- Ujjyaini Mitra (Specialist: Data Science, Career movement to Data science)
- Mario Lewis (Specialist: Data Science, Big Data and Analytics)
- Jose Munoz Mata (Specialist: Data Science, AI, Career Development)
- Harpreet Dua (Specialist: Data Science, Business Analytics)
- Ganapathy Govindan (Specialist: Data Science, Big Data)
- Arihant Jain (Specialist: Data Science, Predictive Modelling)
- Anmol Sunsoa (Specialist: Shaping future in Data Analytics)
If you are coming from a different field, then you can explore mentors, who could help you in your career progression.
To all fresh grads and Data Science enthusiasts, I hope that you find my article useful and you’ll have a good start in your search for a Data Analyst job in 2018. You can post your comments below to discuss on the points that I have highlighted in this article. Good luck!