Predicting Future Market Needs - Industry Today - Leader in Manufacturing & Industry News
 

August 29, 2018 Predicting Future Market Needs

Can you Train your Business Analysts to become Data Scientists?

In earlier days, Business Analyst and Data Scientist terms were used reversibly especially, in the small companies, the line between both of them has got blurred. But, now the era has changed so does the market. Now companies are dependent on Big data analytics for predicting the future market need and move towards it. The place of the Business analyst is replaced by the Data Scientist.

But, why is it happening? Because the machines have taken the place of sapiens. Of course, it makes the workplace autonomous, error-free and minimises task completion time. Through machine learning, data is interpreted and the trends are predicted. How both the job profiles differ? Well, the end result of both profiles is the same. It’s just both of them uses a different tool to achieve the same target. Although, we can say that Data Scientists are more expert in technical knowledge, such as system engineering, mathematical computations, computer programming, and statistics.

While the Business analyst works on their intuition and is biased which makes them to face loss in their career. Also, they are more concerned about previous data and predict on the basis of past results. However, the Data Scientists are more concerned about the future predictions and event happening possibilities. As data analytics has become the backbone of the businesses, companies are searching for potential data scientists who are capable of data mining, data analysing, statistics, data visualisation, and presentation tools. To grab a chance of becoming a Data Scientist one can learn from courses like Data Science Course, such that they would be able to extract the true market value of the business from data.

Transformation of Business Analyst to Data Scientist

We are not endorsing the disadvantages of Business Analyst, In fact, they do have some advantages which makes them important at their own place. They have the highest knowledge which was gained through years of experience, which is vital for analysing the data.

They do have some knowledge about data analysis using database tools and spreadsheets. Also, they are capable of handling any complex information with their strong communication skill. From all these points we can say that it is easier to transform the Business analyst into a Data Scientist. Here, I’m including some basic and easy steps to upgrade the analytical skills:

Improvement in Statistical Approach:

Data Scientist extracts meaning from the collected data set such that they can provide valuable insights to businesses. So you might need to brush up your statistical skills, because the daily routine of data scientist includes extracting data through computerised models, data analysis to improve data quality, observing data patterns, graphs, dashboards, charts and many more.  

Take Help from Crash Courses:

Understand the advantages of the machine learning, how it can be implemented, how it works and how organisations are using it to improve their working models. Because a large number of companies are now using ML for data analysis. With the help of a data analysis course, you will be able to find the accurate and detailed data sets because sometimes data is collected from different sources which is a typical task.

Start Coding Today:

Start from the basics, in case you don’t have any prior programming knowledge. Start practicing and take that knowledge to the next level. Data Scientists are people who develop programs, design algorithms on the daily basis and are capable of developing their own models. So, it is very important for you to get familiar with common programming languages such as Python, R etc.

Practise Harder:

Start with the basic project and you will see with time that your skills are improving day by day. Even you can start implementing the machine learning practices on side projects in spare time. Understand that most of the companies are eagerly looking for data scientist professionals so start improving your resume with analytics work for a better tomorrow.

Join the Voyage of a Data Scientist community:

In the beginning, you might feel alone and isolated because this journey is not that easy, but don’t worry, with time you will learn more things and develop your own skills. Of course, it depends on your learning ability and enthusiasm to become the part of the community. If you feel awkward to meet trainers personally, join the groups available over the internet such as forums, websites and follow professional experts such that you remain updated with the trending technology.

Closure Words:

I personally feel that there is no better candidate then Business analyst to learn the new skills of a data scientist. People who have already taken a proactive step towards learning process are the actual successor of the data analysis. They might be lacking right now but a correct approach in the right direction may take them to the next level. So, don’t worry about getting replaced in the organisation, instead, try to upgrade your skills with machine learning and try to acquire new skill every time.

Renu Bisht is a doyen of governing the digital content to assemble good relationships for enterprises or individuals. Renu is specialised in digital marketing, cloud computing, web designing and offer other valuable IT services for organisations, eventually enhancing their shape by delivering the stupendous solutions to their business problems.

 

Subscribe to Industry Today

Read Our Current Issue

Spotlighting Equipment Manufacturing: Advocate for the People Who Build, Power, and Feed the World

Most Recent EpisodeCADDi: Making Design and Supply Chain Data Accessible

Listen Now

Tune in to hear from Chris Brown, Vice President of Sales at CADDi, a leading manufacturing solutions provider. We delve into Chris’ role of expanding the reach of CADDi Drawer which uses advanced AI to centralize and analyze essential production data to help manufacturers improve efficiency and quality.