Webinar: Emerging Careers in Data Science and Analytics

Why Data Science has the hottest Jobs today? 

In an age defined by data-driven decision-making and technological advancements, there are but a handful of domains that have captured the spotlight as emphatically as Data Science. With every digital interaction, a trail of data is left behind, and within that data lies a treasure trove of insights waiting to be unearthed. It’s no wonder that Data Science has taken the centre stage of the job market, offering some of the most sought-after and compelling opportunities of our time. As organizations across industries recognize the immense potential of data to drive innovation, efficiency, and strategic growth, the demand for skilled Data Scientists has surged, making it one of the hottest job prospects today. Here are a few captivating facts as to why Data Science commands respect for hosting the hottest jobs in today’s times. 

• The data science market size is expected to grow from USD 95.39 billion in 2021 to USD 322.9 billion by 2026. 
• Artificial Intelligence is increasingly becoming popular in business, and companies of all sizes and from all locations feel they need data scientists to develop Al models. 
• According to the US Bureau of Labor Statistics, the number of jobs requiring data skills is expected to grow by 27.9 percent by 2026. 

• About 80 percent of the firms across the globe are investing a large part of their earnings into creating a skilful data analytics division, thus hiring the smartest of people in the industry domain. 
• According to IBM, we are generating data at the rate of 2.7 Quintillion bytes per day and 90% of the world’s data (that’s 90% of all the data ever created) had been created in the previous two years. 

According to the data perspective, about 90% of the data that has been created, have been created from the last couple of years. From the skill perspective, the amount of data created is so humongous, that companies are now sitting on explosive amounts of data and are not sure what to do with the same. Therefore, they are investing actively to find ways to leverage this data goldmine. 

What Is Business Analytics?   

Let’s assume I am a company operating from several years looking forward to take an important meaningful decision that involves a huge risk or a large sum of money. Then you know this decision will not be taken randomly. I will collect all the data available to me, I will analyse all the data I am exposed to, and only after understanding the situation, I will be making that important decision. This whole process of making decisions, after investigating data and making the necessary predications is called Business Analytics.  

In-depth: What Is Business Analytics?   

The business analytics process includes data collection, data mining, descriptive analytics, 

predictive analytics, and visualization and reporting. Business analytics builds on the foundation of business intelligence and attempts to make educated predictions about what might happen in the future using next generation technology. Business analytics uses cloud analytics tools, such as machine learning, data visualization, and natural language query, to process, sort, and present data from various sources. 

Business analytics Process 

Like other emerging technologies, business analytics also use several tools and technologies. There are several steps involved in the process of analysing a data. There are mainly five steps involved in this process and the same will be explained below: 

1. Data collection – We live in 2023, which means data is generated by each and every device like the cameras, your refrigerator or your cell phone etc. From a business point of view, it can be something as simple as a social media copy posted by an organization, or a ThinkPad sensor in a factory, etc. Therefore, there are different types of data that are available in different formats for data collection. This step helps us understand the different kinds of data that can be collected in a process and how the same can be converted to a reliable database which are usually cloud-based. This is very important as data can be in any form, like it would be text based or image based, which needs to be processed into a single database.  

2. Data Mining – Once the data is collected, the next step is to process it. The collected data needs to be sorted, so valuable insights can be gained from the same. This step usually uses machine learning to identify meaningful insights and patters that can be extracted from the data. Using of these technologies, enables a data scientist to identify reliable patters and monetizable trends in a shorter period compared to when the same is done manually.  
    

3. Descriptive analysis – Is all about making sense of the data. This step is basically to understand the story behind the data. The conditions and parameters in which this data was generated. This has a great influence in shaping the direction of our decision.  
    

4. Predictive analysis – Business analytics tools will then start to build predictive ML/DL models based on trends and historical context derived from the above steps. These models can thus be used to inform future decisions regarding business and organizational choices. 
    

5. Visualization & reporting– Understanding a data model is not easy. If you are a data scientist, then you would have without a doubt constructed a reliable model that would give invaluable insights about the business. However, when communicating the same to other teams or business heads, everyone might not always understand the technicality of your decisions. This is where visualization comes into picture. Being able to present the insights obtained for further course of action in an understandable manner becomes very important when multiple teams are in the game. This is done by converting all technical information to easily comprehensible graphs and charts.  
    

What is the major Difference between Business Analytics and Data Analysis? 

Data analytics is an extremely general nomenclature for the process. For instance, if you are solving a problem that uses data, then it’s a normal data analysis process. However, if the analysis and subsequent output is obtained to solve a business problem, then it’s called a business analytics process. 

Also read: Difference between business analytics and data science 

Uses of analytics in the industry! 

Marketing: Analytics to identify success and impact 

Analytics in marketing is used for multiple activities. Which customers are more likely to respond to an email campaign? What was the last campaign’s ROI? More and more marketing departments are trying to better understand how their programs affect the business at large. With Al and machine learning powering analysis, it’s possible to use data to drive strategic marketing decisions. 

Human Resources: Analytics to find and share talent insights 

The need for the use of analytics here is to understand and fulfil the employment needs of an organization as well and employees. It’s to understand pointers like What drives employee decisions regarding their career? More and more HR leaders are trying to better understand how their programs affect the business at large. With the right analytical capabilities, HR leaders can quantify and predict outcomes, understand recruitment channels, and review employee decisions. 

Sales: Analytics to optimize your sales 

What is the critical moment that converts a lead to a sale? In-depth analytics can break down the sales cycle, taking in all the different variables that lead to a purchase. It helps you to breakdown the mind of the consumer at every stage. Price, availability, geography, season, and other factors can be the turning point on the customer journey—and analytics offer the tool to decipher that key moment. 

Finance: Analytics to power predictive organizational budgets 

How can you increase your profit margins? Finance works with every department, be it HR or sales. That means that innovation is always key, especially as finance departments face larger volumes of data. With analytics, it’s possible to bring finance into the future for predictive modelling, detailed analysis, and insights from machine learning. 

Career Options in Business Analytics: Overview and key differences in skillsets 

We all have belonged to a phase where we feel – I do not have experience into this field and I want to go into this field? I am new will I be able to make my career etc. Understanding how your career can span out in the field of data, we must consider it in a top-down approach and identify the different job roles associated with each stage.  

1. Data Collection – If you want to deal with the backend data then becoming a data engineer might be a great choice. As we know data can be in any form – it can be an image data, text or it can be purely numbers. Activities like how to store the data, how do you make it available how do you make queries run really fast, are all a part of data collection and mastering the same opens your opportunity to become a Data engineer. 
    

2. Data Mining – This is usually done by data analyst or a data scientist. These two roles are usually interchangeable. Both the roles have spill over responsibilities and must be equipped in sorting the data or building the relevant ML model. 
    

3. Descriptive analysis – this step in the process is usually associated with business analysts. Here it’s all about understanding the history of the data to address a business problem 
    

4. Predictive analysis – This is ideally the major job description of Data Scientists. Identifying patters and trends and predicting future demands is the basic role of a Data Scientist. 
    

5. Visualization & Reporting – This can be considered the face/front end aspect of the data business.  

Don’t forget:  

Career options with an M.Sc. in Business Analytics 

Top opportunities with M.Sc. in Data Science in India  

Key skill set for a career is data! 

Being a wide domain, data has opportunities to offer access industries. There are only few prominent ones lifted below. However, with niche skill set and affinity towards data, one can carve out successful careers in cross functional teams like – technical predict manager, technical program manager etc.  

1. Data Scientist 
   – Deals with ML modelling 
   – Data exploration using EDA 

2. Business Analyst 
   – Deals with Data Visualization 
   – SQL scripting and EDA 

3. Data Engineer 
   – Backend storage of Data 
   – Big Data Technologies 

4. ML Engineer 
   – Building platforms and features for Data Scientist 
   – More geared towards software development 

Check this out: How to get into the business analytics industry? 

Some common misconceptions about Data! 

Everyone has a lot of confusion about this domain as it is relatively new. Here are a few most common misconceptions about the data science domain and the answers for the same.  

• Data Science is just another name for computer science. Most people often believe that they need to have a computer science background to excel in data science. This is completely untrue. Data science is integrated everywhere. It’s used by lawyers, doctors etc who too have no computer science background. Most data scientists today have picked up this skill by either venturing into the stream and taking a chance, or by pursuing a course which will help them in understanding the basics. 
   

• Data science is dominated by mathematicians. This is a very common misconception because of which many people tend to not take up the opportunity. Advanced mathematics might be needed in some niche part of the job roles, but that is not true for 90% of the job requirements. Most aspect of the data science job requires you to have a keen understanding of basic mathematics of upto 10^th – 12^th standard which most of us would have already delt with in our academic years.  
   
• To succeed in the field of Data Science, you must be a hardcore coder. If this is true one might as well become a software developer, why become a data scientist, correct? To become a successful data scientist, you need to have some basic programming skills. You will not be required to solve complex dynamic problems. 
   
• Business/Domain knowledge is not so important. The requirement is quiet opposite to the misconception. It very important to have the necessary domain knowledge to solve a problem. Although you need not be a maser in the domain you are working on, but having a basic idea is very important. This will in fact help you get a clear picture of the problem being addressed and give you all the necessary insights to build the perfect solution model. 
   
• Learning a tool is most important in Data Science. Well, the idea is to use these tools to make your job easier, not the other way around. There is no one correct tools for all problems. As a skilled professional you must be able to solve a give problem using any tool available. Therefore, there is no point in only becoming a python or a R champion. Develop your skills and be ready to think critically to obtain a solution irrespective of the tools at hand. 
   
• Data Science is all about building models. These very steps and processes od data science will prove it to you its not all about building ML models. Building reliable models is only one of the important aspects of data science, which gives birth to different types and roles for aspirants. 

How to kickstart a career in Data Science? 

1. Choose the right role. There are many roles within Business Analytics Domain like for example – Data Scientist, Business Analyst, Data Engineer etc. Pick a role based on your area of interest. You can do quick research to figure this out before pursuing the same full-fledged.

2. Take up a Course and Complete it. This is good for aspirants across all experience level. Take up a specialization course and complete the same with dedication to understand the latest ongoings of the domain. Also, pursuing a course will help you get a brief insight into all aspects of data management. Then based on your interest you can pick one of the niches and master the same. 

3. Choose a Tool / Language and stick to it with complete dedication. This might sound contrary to what we have said before, but here we are talking about quality instead of quantity. The depth of your knowledge is more important than the width, imagine you know 5+ tools with very basic or no knowledge, instead of just one tool/language perfectly, then you will most definitely not land a job. Therefore, it is important to master a particular tool/language to establish a successful data science career.

Note: This blog is based on insights from Syed Mohammed Ali​, Data and Applied Scientist, Microsoft ​.