Top 9 Highly Rewarding Data Analytics Jobs
We take the help of data in day-to-day life while making decisions. For example, we observe and understand the trends in prices and discounts on various shopping websites. Based on those observations, we manage to shop during a certain period to save money or for better deals. Likewise, for any business, it has become vital to take data-driven decisions to achieve better results. This article consists of fundamental information about the data analytics field and top data analytics jobs available in the world.
According to the US Bureau of Labor Statistics, it is estimated that data analytics jobs and their average income to grow by 23% between 2021-2031. If you are looking to build a career in data analytics but are not sure what job roles are available in the field of data analytics, this article is for you.
What is Data Analytics?
All businesses collect data from various sources like internal systems, social media pages, etc. This data is crucial to understand the trends in the business dynamics and helps to make policies and smart decisions for the future for a sustainable, profitable business.
Data analytics is the process of acquiring, cleaning, analyzing, and visualization of data to make data-driven decisions, instead of just by guesswork and intuitions. For example, a pharmaceutical company decides what medicines to manufacture and when to manufacture them based on the data and trends shown over the period.
Difference Between Data Analysis and Data Analytics
It is very common to use the terms data analysis and data analytics synonymously. However, there is a huge difference between these two terms. Data analysis is a part of data analytics and the related professional is known as the data analyst. Whereas, data analytics is a comprehensive term comprising all the activities that deal with data like data analysis, data science, data engineering, etc.
Types of Data Analytics
Be it data analysis, data science, or data engineering, each one has the following four key types of data analytics. To understand this section, let us take an example of the coronavirus pandemic.
- Descriptive Analytics
In this, the meaning of collected data is understood. The quantitative data in the form of numbers are transformed into the descriptive analysis. It tells the company “what is happening” in the present or “what was happening” in the past.
For example: From numbers or data, initially, medical staff started to observe that certain groups of people are suffering from flu-like symptoms and the disease was spreading from person to person.
- Diagnostic Analytics
From the collected data, once it is clear “what is happening”, the trends and patterns can be found. These trends and patterns are useful to understand “why is that happening” in the present or “why was that happening” in the past.
For example: Once the symptoms are known, medical personnel studied the possible cause behind these infections. They diagnosed that it is a viral infection and spreads through breathing, sneezing, coughing, etc.
- Predictive Analytics
Based on the above two steps, predictions can be done from the data about the events that may happen soon. This type of data analytics deals with “what will happen” in the future based on descriptive and diagnostic analysis.
For example: Once it became known that the Covid viral infections were going on, the medicinal authorities could predict that it would affect a large chunk of the population and might be dangerous for the immunocompromised and old-aged population.
- Prescriptive Analytics
Once the predictive analysis is done from the data, it becomes possible to think of and suggest future strategies, policies, etc. Well-informed decision-making has always proven intelligent and proactive steps to achieve better results.
For example: Once Covid was known to be dangerous for the immunocompromised and old-aged population, it was prescribed to stay at home and other precautions like lockdowns, vaccinations, etc. were started.
Eligibility Criteria for Data Analytics Jobs
There are no fixed eligibility criteria to learn data analytics jobs. Various bachelor’s and master’s courses and degrees in data analytics are available. Generally, they require to have prior education in relevant fields like computer science or engineering or statistics.
There is a range of certificate courses in data analytics available also. Some of them require some technical background in these relevant fields, whereas some of them do not have any fixed eligibility criteria to opt for the data analytics course.
Skills Required for Data Analytics Jobs
There are a few selective skills to be effectively applied simultaneously in data analytics jobs. However, it might be difficult to learn these skills thoroughly and apply them for data analytics jobs. It is recommended to acquire beginner levels of all these essential skills and polish them during internships and entry-level jobs. Here is the list of the skills required for data analytics jobs:
- Structured Query Langauge SQL
SQL is the most essential skill in data analytics. It is used to handle the databases. SQL plays an important role in organizing and updating the data. Dealing with the data using SQL is considered to be the primary yet vital step in data analytics.
However, SQL is quite an easy-to-learn language. Having hands-on experience in SQL is considered to be a must in data analytics jobs interviews.
- R and Python Programming
Programming languages like R and Python are considered to be yet another essential skill for data analytics. These languages are used for cleaning, analyzing, and visualizing the analyzed data in form of graphs, plots, etc. In the beginning, knowing at least one of these languages would serve the purpose.
It is used to build algorithms to find patterns in large datasets. It is not a must-have skill for beginners as data analysts. However, learning this skill gradually would pave an easy path toward advanced-level jobs.
- Probability and Statistics
Probability and statistics from mathematics are used to identify unbiased and accurate ways of finding trends and patterns in the data.
- Data Management
It is the process of acquiring, sorting, cleaning, and storing data. Having the ability to do these data management tasks is essential in the data analytics field.
In this, various mathematical and statistical models are used to identify future trends and patterns based on historical data. This has major relevance in the field of banking and finance. For example, when giving a loan to a customer, econometrics comes into the picture to predict whether that customer will return the money or not based on his historical data.
- Statistical Visualization
After analyzing the data, it is equally important to let people understand the analyzed data. The results from the analyzed data can be represented in the form of graphs, charts, plots, maps, etc.
Popular Tools for Data Analytics
A wide range of tools is used in the field of data analytics for mining, cleaning, analyzing, interpreting, and visualization of the data. Here is the list of popular data analytics tools.
- Microsoft Excel
- R and Python
- Microsoft Power BI
- Apache Spark
- Jupyter Notebook
Industries Recruiting Data Analytics Experts
Although there are a lot of data analytics jobs open across the world, there is a shortage of skilled workers in this field. Hence, companies and organizations from various fields are looking for skilled data analysts. Presently, the following industries are welcoming skilled workers in data analytics.
- Banking & Securities
- Healthcare & Pharma
- Information Technolgy
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Top Recruiters of Data Analytics
Once you are ready with all the essential skills for data analytics jobs, data analytics recruiters are waiting out there! Because of a combination of the facts of huge needs and limited availability of skilled workers in these fields, the recruiters are countless. Here is a list of some of the top recruiters in the field of data analytics.
Data Analytics Jobs
Once you are equipped with the necessary data analysis skills, a plethora of jobs become open in various fields like finance, healthcare, IT, etc. Some of the jobs are entry-level whereas some are advanced-level jobs. Here is the information about the data analytics jobs roles, job descriptions, and salary range.
1. Data Analyst
The demand for data analysts is huge and going to rocket in the decade. According to the World Economic Forum, data analysis is the number two growing job in the US. This is an entry-level job in the data analytics field.
There are no fixed eligibility criteria for a data analyst. A certificate course in data analysis, a diploma or a bachelor’s degree in computer science, software engineering, or other relevant fields are the best suitable paths for data analysts.
However, people from other backgrounds can also learn the skills through various university degrees, online courses, etc., and become eligible for this job role.
- Identify the problem: For data analysts, it is a crucial part to identify the exact question and purpose before starting actual data analysis.
- Collect the data: Once the question or purpose behind the data analysis is identified, data analysts collect the relevant data confined to their problem. Data analysts often acquire data from the company’s website, company’s social media platform, or sometimes buy from data-providing companies.
- Clean the data: These datasets are collected in their raw form along with outliers, duplicates, and errors. Cleaning this data and drawing meaningful, relevant data is the next step. SQL is a widely used tool for handling large databases.
- Analyze the data: Using various statistical and probabilistic models, data analysts analyze the cleaned data. They commonly use R and python programming languages for this task.
- Interpret the data: After the data analysis, data analysts try to find out the trends and patterns that could give the results or solutions to the originally identified question.
- Visualization of data: To simplify the results and make them understandable to related people, data analysts put the results in the form of graphs, plots, maps, charts, etc. For this, they commonly use the tools like Tableau, MS Excel, and Jupyter Notebook.
Average salary: According to Glassdoor, data analysts in India earn an average of$72,000 in the US and INR 4.2 Lakhs per year. It may change with experience, location, and the hiring company.
Job roles: A data analyst can have the following different job titles:
- Healthcare Analyst
- Business Analyst
- Financial Analyst
- Business Intelligence Analyst
- Quantitative Analyst
- Marketing Analyst
Must check out the top-ranked Data Analytics Courses in India
2. Data Scientist
Like data analysts, data scientists also primarily involve in finding trends and patterns in data. However, data scientists have more responsibilities and complex problem-solving as compared to data analysts. Data scientists are generally supposed to find questions or goals for the company on their own for data analysis, whereas data analysts assist the data scientists in analyzing the data on the already set questions.
Eligibility Criteria: It is preferred to have a bachelor’s or master’s degree in data science. Or one can start a career as a data analyst and take it up to a data scientist with skill enhancement. It is not an entry-level job.
People with PhDs in mathematics, statistics, and physics are also considered to be the best candidates for data scientist jobs.
- To identify questions to be asked by an organization to achieve better results
- To find trends and patterns in the databases
- To build algorithms, and models using advanced machine-learning techniques
- To develop predictive models to understand future trends and patterns
- To upgrade the quality of the organizational data
- Programming languages like R, Python, SQL, SAS, etc.
- Visualization skills like MS Excel, Tableau, and PowerBI
- Machine Learning
- Hands-on experience with software like Hadoop, Apache Spark
Average Salary: According to Glassdoor, an average salary of a data scientist is INR 10 lakhs in India and $1,21,238 in the US per annum. It may change with experience, location, and the hiring company.
3. Data Engineer
They are primarily involved in building and testing stable and sustainable systems with which data analysts and data scientists can work.
Most data engineers start their careers as software engineers and then with skills and experience become data engineers. So, a bachelor’s or master’s degree in software engineering is the most suitable background for a data engineer.
A data engineer is not an entry-level job. However, with experience and expertise in data analytics skills, one can move towards this job role gradually.
- Design and test data management systems
- Maintain and update the data management systems that are already in existence
- Research and remain updated for the new technologies in the market for system upgradation
- Programming languages like SQL, NoSQL, Java, C++, Matlab, Scala, etc.
- Hands-on expertise in big data tools like Kafka, Hadoop, MongoDB, etc.
- Cloud Computing
- Automation and Scripting
- Machine Learning
- Data Security
Average Salary: According to Glassdoor, the average salary of a data engineer is INR 8.0 Lakhs in India and $1,14,525 in the USA per annum. It may change with experience, location, and the hiring company.
4. Data Architect
Typically a data architect is involved in managing the infrastructure of data management systems.
Eligibility Criteria: It is not an entry-level job. A bachelor’s or master’s degree in data architecture, data engineering, software engineering, and management work the best background for a data architect job.
- To maintain database systems considering the organization’s security
- To audit and upgrade the performance of the existing data management system
- To execute the solutions to store and manage the organization’s data
- To plan databases for future
- Machine Learning
Average Salary: According to Glassdoor, an average salary of a data architect is INR 22 lakhs in India and $1,27,666 in the US per annum. It may change with experience, location, and the hiring company.
Job roles: A data architect can have the following different job titles:
- Cloud Architects
- Security Architects
- Machine Learning Architects
5. Database Administrator
A database administrator should have strong technical knowledge of database structures and their installation procedures.
Eligibility Criteria: A bachelor’s or master’s degree in computer science or software engineering.
- To Maintain, and track the performance of the company’s databases
- To implement suggested upgrades and corrections in the existing databases
- To maintain database security, integrity, stability
- To keep database backup and recovery provisions
- Microsoft SQL Server
- IBM DB2
Average Salary: According to Glassdoor, an average salary of a data administrator is INR 7.5 lakhs in India and $97,514 in the US per annum. It may change with experience, location, and the hiring company.
6. Data Analytics Manager
Typically, a data analytics manager works with a team of data analysts and keeps track of the company’s data analytics work and overall progress.
Eligibility Criteria: A bachelor’s or master’s degree in data analytics, computer science, or in a relevant field.
- To plan strategies for business intelligence and data analytics
- To track data analytics progress
- To come up with solutions to improve business data analytics
- To transform raw data into useful results for deciding the company’s strategies and policies
- Data Analytics Skills
- Management skills
Average Salary: According to Glassdoor, an average salary of a data analytics manager is INR 20 lakhs in India and $150,500 in the US per annum. It may change with experience, location, and the hiring company.
Also, read about the best Online Data Analytics Courses
A statistician is involved in working with converting numerical data and numbers into valuable results to make a company well-informed about its performance. They use various statistical models for this task.
Eligibility Criteria: A bachelor’s or master’s degree in statistics or mathematics or data analysis
- To create and maintain databases using statistical models
- To check data integrity, errors in the process of cleaning the data
- To ensure the validation of the data
- To visualize the data in the form of charts, plots, graphs, etc.
Skills required: Statistical programs like SAS, SPSS, R, and Stata.
Average Salary: According to Glassdoor, an average salary of a statistician is INR 5 lakhs in India and $93,700 in the US per annum. It may change with experience, location, and the hiring company.
8. Machine Learning Engineer
Machine learning is a branch of artificial intelligence. A machine learning engineer is not an entry-level job. It requires years of experience to become a machine learning engineer. A machine learning engineer is an integral part of the data science team. Data scientists make use of models developed by machine learning engineers.
Eligibility Criteria: A bachelor’s degree in computer science or software engineering for an entry-level post. A master’s degree, Ph.D. in computer science, data science, or software engineering for advanced-level posts.
- To build and test machine learning models and algorithms for data science
- To update the machine learning models and algorithms
- To perform statistical analyses
Average Salary: According to Glassdoor, an average salary of a machine learning engineer is INR 8 lakhs in India and $1,36,400 in the US per annum. It may change with experience, location, and the hiring company.
9. Project Manager
Typically project managers are involved in basic data analysis to keep track of the progress, productivity, efficiency, and skills of employees. These are often management posts with the requirement of the minimum or the least data analytics knowledge.
Eligibility Criteria: A bachelor’s or master’s degree in management and knowledge of data analytics fundamentals.
Average Salary: According to Glassdoor, an average salary of a project manager is INR 15.5 lakhs in India and $91,000 in the US per annum. It may change with experience, location, and the hiring company.
Is data analytics hard to learn?
Data analytics expertise requires mastering the skills like SQL, R, python, machine learning, Tableau, etc. which can be complex and hard to excel in the beginning. However, beginner levels of these skills are quite easy to learn. One can take up beginner-level jobs and master their advanced levels over time.
How much time is required to learn data analytics?
The time required for learning data analytics depends on various factors like any prior skills you have or not, whether you are learning it full-time or part-time or on your own, etc. however, generally speaking, learning beginner-level data analytics can take up to three to six months.
Can data analysts’ jobs be done remotely?
Once you are an experienced data analyst, you can work independently from your home. However, an entry-level data analytics employee would need face-to-face support from relevant co-workers which it becomes easier to work in the office.
Is mathematics required to learn data analytics?
Linear algebra, statistics, and probability are the fundamental pillars of data analysis. Machine learning is the backbone of data science which requires writing algorithms that require math. One doesn’t need to have a formal degree in mathematics, but one should have good hands-on linear algebra, calculus, statistics, and probability.
Is coding knowledge required for data analytics jobs?
For entry-level jobs like junior data analysts, advanced-level coding is not needed. Basic levels of SQL, R, or Python knowledge are sufficient which is straightforward to learn. However, as you grow your career toward data science, and data engineering, advanced coding skills become essential.
These days, nearly every business collect and analyze its data and prefer to make data-driven policies and strategies. By 2028, it is expected that about 70% of businesses and companies would hire data analysts. Hence there is a boom in data analytics jobs and the demand for data analysts is expected to rocket soon. However, there is a great shortage of skilled data analysts. If you have a strong desire and passion to learn data analytics techniques, a wide range of data analytics jobs are available with the potential for career growth. I hope this information will be useful to understand the scope of various data analytics jobs.