A Detailed Guide to the Data Analytics Career Path

Introduction

Are you looking to make your career as a data analyst? Are you already in an entry-level position and wondering what comes next?

Regardless of where you stand, if you think that data analytics is the right career for you, it can be a good idea to familiarize yourself with the Data Analytics career path.

In the 21st century, the ‘data analytics’ has become one of the most buzzing terminologies!  

As per The Global State of Enterprise Analytics, a report by business intelligence company MicroStrategy, 56 percent of respondents said data analytics led to “faster, more effective decision-making” at their organizations. 

It’s a powerful tool that’s available to companies at a staggering scale. When harnessed correctly, data has the potential to drive decision-making, impact strategy formulation and improve organizational performance.

So are you all set to go for the data analytics career option? And have questions like:

• What is data analytics?
• What are the benefits of choosing Scrum Master as a career option?
• What is the future scope of data analytics? 
• What are the required certifications to become a data analyst?
• How to become a data analyst?
• What is the salary of a data analyst?

In this blog, you will get answers to all these questions, so continue reading and why, when, and how to become a data analystr: 

What is Data Analytics?

Data analytics (DA) is the process of examining data sets in order to find trends and come up with conclusions about the information they contain. Increasingly, data analytics is done with the support of specialized systems and software.

It allows companies to analyze all their data (real-time, historical, unstructured, structured, qualitative) to identify patterns and generate insights to inform and, in some cases, automate decisions, connecting intelligence and action.

Since the process of analyzing raw data to identify trends and find the answers to the questions, the data analytics meaning captures its broader scope.  

It includes numerous techniques with multiple goals. The data analytics process has a few components that can help in different initiatives. When you combine these components properly, you get a successful data analytics initiative that will help you know where exactly you are, where you have been, and where you should go. 

In other words, you can understand what is data analytics – as a form of business intelligence that is widely used to resolve specific challenges within a company. It is focused on finding patterns in a dataset that tell you something meaningful about a specific business area– how certain customers behave.  

Throughout this post, we will delve into what is data analytics, its types, how data analytics helps business and data analytics career paths.

What are the Different Types of Data Analytics?

Now we are clear on the data analytics definition, let’s go through the types of data analysis: Diagnostic, Descriptive, Prescriptive, and Predictive. 

Diagnostic Analytics: Diagnostic Analytics explores the “why” – it helps to identify anomalies within the data – that is, anything that cannot be explained through data in front of them. Taking the analysis a step ahead, Diagnostic Analytics is used in comparing coexisting trends or activity, finding correlations between variables, and causal relationships where possible. This analytics is effective to identify the root of an organizational issue.

Descriptive Analytics: Unlike diagnostic, descriptive analytics is a simple surface-level type of analysis that focuses on what has happened in the past.  The major techniques used in the process include data aggregation and data mining – so, the data analyst first collects the data and presents it in a summarized way and then does the data mining to find the patterns. In the next step, the data is presented in a way that can be easily understood. 

Prescriptive Analytics: Prescriptive analytics provide suggestions on the actions that need to be taken. Prescriptive analytics supports how to take advantage of the results that have been predicted. While conducting prescriptive analysis, you need to consider all the possible scenarios and assess the various actions the company might take. Prescriptive analytics is considered as one of the more difficult kinds of analysis and may involve working with algorithms, ML, and computational modeling procedures. The effective use of prescriptive analytics can bring a huge impact on the decision-making process of the company and, ultimately, on the bottom line. 

Predictive Analytics: As the name suggests, predictive analytics presents what is likely to happen in the near future. Here, data analysts bring data-driven and actionable insights that the firm uses to inform their next step. Predictive analytics estimates the future outcome based on historical data and probability theory. This kind of analytics is used to forecast all sorts of outcomes – from what products will be popular at a certain time. 

The type of analysis you carry out will depend on the kind of data you are working on. 

How can Data Analytics Improve Business Decisions?

Data has emerged as the keystone in the strategic business decision-making. It enables businesses to make consumer-oriented marketing decisions to help them address key operational inefficiencies. Also, the right use of data helps you make strategic and critical business decisions that will immensely improve organizational performance. 

Let us take a deep dive into how data and analytics bring diverse opportunities to businesses today:

Better Customer Experience

First and foremost, data and analytics-based decisions allow you to understand employee and customer interactions, and work with your tech team to make those interactions better. When the IT team connects with the marketing teams, it helps achieve audience goals. So the company better allocates budgets based on customer response. With a deep understanding of customers, you get the ability to create the right messages to the right audience at the right time.

Drive Performance

Data and analytics play a significant role in reducing inefficiency and streamlining business operations. For instance, it provides managers with complete insights to do cost valuations, peers benchmarking, and pricing segmentation. Similarly, analytics is used to measure key performance metrics in operational excellence, product innovation, and workforce planning. It allows you to come up with calculated insights to resolve complex business scenarios.

Increased Transparency & Accountability

Data-driven decision-making effectively enhances teamwork and employee engagements. There are fewer mistakes because of misunderstandings. The employees who know exactly what’s going on, what their role is, are more likely to suggest improvements. All because they understand the current state of the business and long-term goals.

Risk Management

Today’s businesses are exposed to immense risk of data leakage due to the unstructured data on websites, blogs, and social media channels. By leveraging risk analytics, you can effectively quantify, measure, and predict risk and incorporate risk considerations into your core strategic decision-making process. It makes risky business decisions more uniform, enhances the quality of data, and provides better agility to address unconventional data needs.

Examples of How Data Analytics is used –

Google is focusing on a new concept called, “people analytics.” In one of its people analytics initiatives, Project Oxygen, the company mined data from 10,000 performance reviews and compared it to employee retention rates. Then, it used the information to identify the general behavior of effective managers and developed training programs to create competencies. 

Overall, a data and analytics-based approach empower you to respond to market hurdles quickly. It helps companies make decisions on the basis of exact numbers and predict outcomes in different business segments with better accuracy. It has become a mandatory tool to drive growth, beat the competition, and get loyal customers. 

There is an increase for professionals who can help businesses convert raw data into meaningful and organized info, which in turn can help in their growth. There are several job roles in the data analytics field, and among the work profiles, becoming a data analyst provides the most amazing career opportunities. 

So what’s next: understand what data analysts do: their roles and responsibilities and required skillset. Let’s go!

Roles and responsibilities and Skills Needed to Become Data Analyst

The data analyst profile includes a bunch of roles and responsibilities. And the first step towards becoming a data analyst is to know the roles and responsibilities of one! 

Understanding the Business Objective

The very first responsibility of a data analyst is to identify the organization’s goal. They are responsible to assess the available resource, identifying the business problem, and collecting the right data. 

Data Mining

Data mining is another major task when you decide to work in data and analytics. Here, data is mined from numerous sources and organized to find new details from it. With the mined data, data models are built to boost the efficiency of the system.  

Querying

Data analysts have to write complex SQL queries and scripts to collect, store, manipulate and retrieve data from relational databases like MS SQL Server, MySQL, and Oracle DB. 

Data Cleansing

Data cleansing is another vital duty of data analysts. When the data is gathered initially, it will be messy with missing values. Hence, It is crucial to clean the collected data and make it prepared for the analysis. 

After you have explored enough about the roles and responsibilities of data analysts, let’s go through the skills and abilities that data analysts are expected to demonstrate. 

Data Examining

Data analysts utilize numerous analytical and statistical tools including programming languages to carry out a logical examination of data. 

Major Hard & Soft Skills of Data Analyst

Here are some of the most major hard and soft skills you’ll need to become a data analyst:

Mathematical and statistical ability: Data analysts have to spend a large portion of their time working with numbers, so having a mathematical and statistical brain is almost compulsory!

Knowledge of programming languages: Data analysts rely on a number of programming languages to carry out their work. That’s why, having knowledge of some basic programming languages like SQL, Oracle, and Python is required.  

An analytical mindset: If you want to be a data analyst, just crunching the numbers and sharing your findings is not enough.  You should be able to understand what’s going on and dig deeper if necessary. An analytical mindset is a must!

Keen problem-solving skills: As discussed above data analytics is all about answering questions and resolving business challenges, and data analyst should have some keen problem-solving skills.

Excellent communication skills: Once you’ve harvested your data for valuable insights, now it comes to sharing your findings to benefit your business. Data analysts work together with key business stakeholders, and sometimes share and present their insights to the entire company. For this, you must have excellent communication skills. 

By now, we’ve got a good understanding of the roles and responsibilities of a data analyst and the required skill, you are ready to unlock the secret - how to become a data analyst? 

How to Become a Data Analyst: Step-wise Process

If you like what you’ve explored so far, it’s time to think about how you can make it a reality. In this section, we’re going to answer that all-important question: How to become a data analyst.

A career change can be a daunting prospect, so we’ve broken it down into five simple steps to take you from aspiring data analysts to fully-fledged professionals. Here’s your step-by-step plan of action!

Step 1: Understand the Fundamentals

This first step is all about getting familiar with the fundamentals – what is data analytics and knowing some of the key tools and principles. 

Read the data analytic theory as much as possible, familiarize yourself with the kinds of analyses it includes—descriptive, diagnostic, predictive, and prescriptive—and getgrips  with the various data analytics techniques, like regression analysis, factor analysis, cohort analysis, cluster analysis, and time-series analysis. It provides a good theoretical foundation upon which to build those all-important practical skills. 

Step 2: Take the Next Step with a Structured Course

The next step in order to become a successful professional in data and analytics is to take a specialized data analytics training course.

With the rising demand for skilled data analysts, there are variety of courses to choose from. But, not all data analytics courses are created equal, so you need to be careful when choosing a program  that can complement your schedule and fits your budget. Here are some important aspects you need to keep in mind while going for any course;  

• The courses that just does not talk about what is data analytics and its basics but can provide one-to-one mentorship helps you to stay motivated and getting genuine feedback on your work.
• A practical, hands-on curriculum is another major requirement. Apart from providing the theory look for a course that will test your knowledge continuously with practical exercises.
• If your primary goal is to get hired as a data analyst, choose the courses that offers career coaching and a job guarantee, ensuring you find the right role once you’ve finished your studies. 

Step 3: Hone your soft skills

While the training course takes care of hard skills, it’s a good idea to start honing your soft skills.

Think about the soft skills you’ve already perfected and find those that might need more efforts. Suppose, you have excellent research and problem solving skills as part of your current role, but haven’t had much experience on giving presentations. If you find this kind of opportunity within your current workplace, grab it as soon as possible. You can offer to run a small research project and provide your findings to the team by offering to tackle a specific business issue.

Step 4: Start networking

Good networking is considered as the golden rule for every industry!

As an aspiring data analyst, focus on connecting with like-minded people as early as possible. 

A good network not just open up potential doors in terms of career prospects but this is also an excellent source of mentorship and support as you find your feet in a brand new industry. 

So where to begin? Here are some great ways of networking:

LinkedIn - Join some data analytics groups and connect with other professionals of data analytics field. 

Student community – Various data analytics courses provides you ways to get in touch with the wider student communit. This is a perfect option to lend and receive peer-to-peer support and also make offline connections.

Step 5: Polish your portfolio and Get Ready

The last step towards making your career in data and analytics is to refine your portfolio and become ready for the job market.

The portfolio is the most important asset when it comes to applying for jobs; it presents how you work and demonstrates to employers that you’ve mastered the certain practical skills and know applying them in the real world. 

Aside from your portfolio, your digital presence should also be optimized for your new career as a data analyst. It includes updated LinkedIn profile highlighting all the relevant skills and tools, and a summary of the kind of profile you’re looking for. 

Once you start applying for jobs or data analyst internships, it’s a good idea to prepare for the interview process. To help you prepare, we’ve put together Top 30 Data Analyst Interview Questions And Answer. 

Best Data Analytics Certifications & Courses

As more and more companies have recognized the significance of data, data analytics has become a  today buzzword. With the increasing demand for data analyst,  a whole host of courses and programs are emerging. These training courses help you learn about data analytics from scratch and facilitating a career-growth in this field. It’s a perfect time to be an aspiring data analyst! So what courses are worth considering? 

Here are some of best data analytics courses below: 

The Certified Analytics Professional (CAP) Credential: A general certification provided by INFORM that help you to certify and formalize your skills if you already have some experience in data analytics. 

Master Analytics Tools and Techniques With Data Analytics Course: This course is focused on the basics and teach the fundamentals of the Data Analysis framework to help you become intimate with the roles, events, artifacts, and rules of an ideal Data Analyst.

Data Science Training - Using R and Python: The training course helps you get practical learnings from basic to advanced level focused on Data Science methods with R & Python, machine learning, AI, deep learning, Big Data Hadoop, and Tableau Data Visualization in complete depth.

Master Analytics Tools and Techniques With Data Analytics Course: The course helps you through the data visualization, data mining, refining and interpretation. It is focused on the basics and teach the fundamentals of the Data Analysis framework so that one can become intimate with the roles, events, artifacts, and rules of an ideal Data Analyst.

Methods and Tools you Need to Learn as a Data Analyst

As the field of data analytics is evolving, it is not just limited to what is data analytics, the variety of data analysis tools grows with it. If you’re considering a career in data and analytics field, you’ll want to know: Which data analytics tools do I need to learn? Below we have highlights some of the key data analytics tools you need to know: 

SQL

SQL is one of the most prominent tools you need to learn as a data analyst. It is a programming language that was initially used for managing data from a relational database. It is a straightforward tool to learn and can be used for complex, challenging data analytics. 

Analytics Tools: Excel, VBA and Matlab

A classic in the world of data analysis, Excel tops the list as a crucial tool to learn as a data analyst. A data analysts should be proficient in Excel from using formulas to creating pivot tables. 

VBA (Visual Basic for Applications), a programming language specifically designed for Excel and is used in financial analysis.

Matlab is another useful tool used for creating algorithms, building models and analysing data.

Python

An open-source programming language, Python allows you to build and manage data structures. It is a versatile tool that supports data manipulation, data analysis and data representation. With python, you can visualise data collected by presenting it in charts or graphs, test a hypothesis through A/B testing and to evaluate the data collected.

KNIME

Konstanz Information Miner or popularly known as KNIME is a free platform for data analytics, reporting, and integration platform especially created for analytics on a GUI based workflow.

Tableau

The market-leading Business Intelligence tool, Tableau is widley used to analyze and visualize data in an easy to understand way. The tool allows you to work on live data-set and spend more time on Data Analysis instead of the Data Wrangling.

Having read about what is data analytics, what a career in data analytics entails and the skills you’ll need to master, you may now be wondering how much data analyst make? In the next step, we will check the data analyst salary in different nations and the future scope. 

Salary of Data Analyst & Future Scope

By now, you are aware of what is data analytics, its different types, career scope in data and analytics and a lot more. Becoming a data analytics can be  the best decision you can take for your career. Many giants brands are watching scope in the data analysis field. Here are some highly acclaimed companies hiring data analytics experts – 

• Google
• Axis Bank
• Micro-soft
• Kotak
• Cartesian Consulting
• ICICI Bank 
• Accenture
• Amazon
• Facebook
•  Instagram
• Snapdeal
•  Flipkart

IBM has declared data analytics as a trending job of the 21st century and predicted that the demand for Data Analyst will rapily boom in the near future.

The sharp rise in the demand of data analyst is testament to what a significant a data Analyst plays in any organisation. Needless to say, the role of a data Analyst cuts across all divisions of an organisation. 

Apart from the increasing demand, data-related roles often come with high paychecks. The reason is the field of data analytics is complex and still evolving, resulting in only a few skilled professionals. Due to this shortage of talent in data analytics, companies are ready to pay high salaries even for entry-level professionals

The salaries of data analyst can vary depending on education level, experience, industry, and location. While salary averages are constantly fluctuating, the average annual salary for data analysts in the United States is $70,033 annually. Average annual data analyst salary in different nations: 

• India: ₹9,50,000
• UK: £27000
• Australia: A$82K 
• Singapore: S$55K
• Germany: €45,338

Working as a data analyst can be a difficult work profile that involves extracting and analyzing insightful information from large data sets. 

Key Takeaways on What is Data Analytics

Today’s business decisions powered with data insights is very important in being successful and maintaining the competitive edge. Here, the role of Data Analysts is vital, providing organizations this competitive edge.  

The post covered everything you need to know to make career in data analytics. Now that you know;  

• What is data analytics?
• How to become a data analyst
• Roles & responsibilities of a data analyst
• Salary & future scope in data analysis

Now, you’re all set to start your learning journey and prepare for a data analyst career path. So what are you waiting for? Your dream job role is a click away. Start learning now!

FAQs on What is Data Analytics

Q1. What is data analytics?

Ans:- The easiest way to answer what is data analytics – it is the pursuit of finding meaning from raw data through specialized computer systems. These systems transform, organize, and model the data to draw conclusions and also identify the patterns.

Q2. What is the difference between data science vs data analyst? 

Ans:- A data analyst works with structured data to solve tangible business issues by using tools like SQL, R or Python programming languages, while a data scientist works capturing and analyzing data to be used by the analysts. 

Q3. What is differencet between business analyst vs data analyst? 

Ans:- Business analysts and data analysts both need to work with data. The difference is – business analysts utilize data to make strategic business decisions. Data analysts are responsible to collect data, manipulate it, find useful information, and convert the findings into digestible insights.

That’s it for now…

Here is hoping that you got enough enlightenment about it. If you have any doubts on this post ‘What is Data Analytics: A Comprehensive guide’ mention them in the comment section below, we will be happy to help you!

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