We have decades of experience in upskilling students, interns and even experienced managers in Data Analysis. We have trained thousands to unlock the power of data and give sustained competitive advantage to the organizations they join.
During our training, the most common concerns that prospective students and those at the start of the data analytics journey are the below. Most likely these will be your concerns too.
Can someone with little or no knowledge of statistics ever become good at Data Analytics?
What skills, knowledge and coding or technical expertise I need to develop to become a “good” Data Scientist enough to get jobs in top corporates?
Before we clarify the above questions we have to first have an clear industry definition of a data analyst.
There are multiple interpretations of the word “data analyst”. Different industry experts mean completely different skillsets when defining data analyst. To add to the confusion the terms “Business Analyst”, “Data Scientist” and “Data Engineer” are also used widely. So what is the difference between a data analyst and business analyst? And how much of technology including software, hardware, coding is required for a data analysts job?
After interactions with multiple industry expert, we have arrived at the high level definition of a data analyst, summarised in the venn diagram below. This shows the roles of the 3 common terms Business Analyst, Data Scientist and Data Engineer.
Figure 1: A high level view of overlapping knowledge and skillsets required by the Industry.
We will cover the roles of Business Analyst, Data Scientist and the Data Analyst who is at the intersection of the two. We will however not elaborate the role of a Data Engineer (also called Data Architect etc.) in this blog.
As evident from above diagram, a Business Analyst as the term suggests is an expert on the “domain” ie. on the industry, specific processes, nuances of the industry and the way her organization runs. She has a high level overview and is able to understand the external market forces, competition, how her organization sells and delivers the products or services and internal processes and systems. Her focus is purely on the Customer ie. how to improve customer experience and therefore grow the company profitability. Does a Business Analyst need to manage data? Of course – she has to use data to analyse the current state “As Is” state and plan improvements to reach the final “To Be” ideal state. Data is just a measure of her organization success / failure to meet customers’ needs. Interpreting the results of data to arrive at actionable insights is absolutely essential to the career success of a Business Analyst.
A Data Scientists role is all about managing the data pipeline to provide data to solve any business problems. Please refer to the next blog on Knowledge Discovery in Databases (KDD) for more information on the power and role of a Data Scientist.
So what then does a data analyst do? A Data Analyst role focuses on data into assets for the organization. She needs to work closely with the Business Analyst to understand what the business objectives are, gather the required data, do the necessary how to capture data from various sources, transform and analyse.
A Data Analyst is therefore at the intersection of a Data Scientist and a Business Analyst. A data analysts focus is all about interface between business and Data Science. They need to know the concepts of statistics, data science, Machine Learning Pipeline and also needs of Business to use relevant data to derive insights and solve the business problem.
Blogs in this series:
1. Who is a Data Analyst
2. Knowledge Discovery in Datasets.
3. Roadmap from a Novice to a valuable Data Analyst.