Today, data has become an integral part of our lives. Companies use the data produced every day for optimizing their strategies. And to help them make sense of the data and get insights, they need a Data Analyst. 

Data analyst is responsible for processing the data related to the products, customers, and performance of the company. Once this is done, they are able to release the indicators used by decision-makers. It is the information provided by the data analysts that allow the companies to define their strategies and products to meet the needs of the customers. 

You can say that Data Analyst is one of the most important jobs in the data age. It’s their job to process data, refine it, and then obtain actionable insights that can be used for decision-making. And another popular aspect behind the popularity of this job is the high income that comes along with it. The reason behind this is increased demand and low supply. Because of this, we have created this guide to becoming a Data Analyst in 2020. To begin with, we should first understand the difference between a Data Analyst and a Data Scientist as there is a lot of confusion between the two roles.

Difference between a Data Analyst and a Data Scientist

Both of these roles have a data-related job. What exactly is that job? 

As a Data Analyst, you will be using the data to solve problems and get actionable insights for the company. To do this, you will apply different tools on well-defined datasets for answering questions like “Why did the sales reduce in the last quarter?” or “Why has a certain marketing strategy been effective in certain areas?” and so on. To answer these questions, you need to have basic skills like Statistical Analysis, R, SQL, Data Analysis, Data Mining, etc. 

The job of a Data Scientist, on the other hand, involves designing new algorithms and processes for data modeling, creating predictive models, and performing custom data analysis as per the requirements of the company. To be a Data Scientist, you need to have all the basic skills of a Data Analyst along with others like Machine Learning, Deep Learning, AI programming, etc. 

The primary difference between the two roles is that a Data Scientist uses heavy coding to design modeling processes unlike a Data Analyst, who uses pre-existing processes for obtaining insights from data.

Skills needed to become a Data Analyst

A Data Analyst must have the skills of finding a needle in a large volume of data haystack. Here are some of the skills that you will need to do the same:

1. Python

As a Data Analyst, you must have programming skills. You will use it to perform predictive analysis on big data sets so that you can draw useful insights. The most common languages used for this are Python and R. Python is used for data analytics because of its easy readability and capacity for statistical analysis. R is more popular because it was created for data analytics specifically.

2. Data Analytical Skills

Now, this one is obvious. What is the point of being a Data Analyst if you don’t have Data Analytical Skills? But what exactly are Data Analytical Skills? It is the ability to analyze and interpret large volumes of data and produce actionable insights for the organization. To do this, you will need the basics of Statistical Analysis. There are many analytical tools like Spark and Tableau that can help in Statistical Analysis. So, you should also have a deep understanding of them.

3. SQL

Since data plays a big part in Data Analysis, so does Data Management. For this, you must be proficient in SQL, the most common tool used for Data Management involving Extracting, Transforming, and Loading. It will be your job to extract data from different sources, transform it in the required format so that it can be analyzed, and load it into the data warehouse. Apart from this, SQL is also used for running queries that help in finding relevant trends in the data.

4. Advanced Microsoft Excel 

Microsoft Excel is more than a simple spreadsheet. It is an important Data Analysis tool. Now, you can’t use Excel for big data analytics like Python or R. But, it is the perfect tool for smaller analytics with other tools like VBA methods. It is important that you learn about the functions available in Excel. It has remained relevant in Data Analytics for several years and is essential for a successful Data Analysis career.

5. Communication skills

Communication skills are also a must to become a Data Analyst. The reason behind this is that you will be the one who understands the data better than everyone. So, it will be your responsibility to translate the data findings into quantified insights that a non-technical team can use for decision-making. Consider this as data storytelling. You will be presenting the data in the format of a story with values and concrete results so that other people can understand it.

Career Paths for becoming a Data Analyst

There is no single, fixed path for becoming a Data Analyst. There are several paths for reaching your goal and you can follow any of them. The most basic and common path is completing a Bachelor’s degree in Data Science. The course will teach you all the skills needed for collecting, analyzing, and interpreting large volumes of data. You will also learn about programming languages, analysis techniques, and statistics, etc. as they help you in your job.

Another option that you have is to obtain any technical degree in any subject related to Data Analysis like Computer Science, Mathematics, Statistics, Economics, etc. After this, you can go for a Master’s degree in Data Science, Big data, Business Analytics, etc. All of these streams will aid you in your Data Analytics job. You can also do an internship in Data Science after your Bachelor’s degree for getting the practical experience you need. It is a great way for learning on the job and making connections. Some candidates go for a Data Analytics certification course. A benefit of going through this path is that you will be able to learn about the field while working a full-time job. No matter which path you choose, as long as you are motivated, you will be able to become an expert Data Analyst.

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In the modern data-centric world, businesses seek to fully capitalize on the use of the data they generate in every day processes. This burgeoning need to gain access to data assets has given the rise in demand of data scientists. They are capable of analyzing and processing the voluminous amount of data to derive valuable business insights from it. A data scientist is not only limited to transform IT systems, but also poised to influence sectors, from retail and healthcare to telecommunication, agriculture, and mobility, among others.

Undeniably, the field of data science is very vast in terms of skills and job roles and spans Data Analyst, Data Engineers, Database Administrator, Machine Learning Engineer, Data Scientist, Data Architect, Statistician, Business Analyst, and Data and Analytics Manager.

The demand of data scientists is continuously growing. Considering data from LinkedIn, Data Scientists ranked first among the most promising jobs in the United States in 2019. They have the ability to assist businesses to interpret and manage data and solve intricate problems using expertise in a variety of data niches. Their roles are becoming more indispensable to even traditional organizations that did not previously invest much of their budgets in technology.

What Really Defines a Data Scientist?

The role of a data scientist can be varying based on industries’ business objectives and goals. He/she must have the ability to comprehend the business problem or decision that helps model and abstract what is critical to solving the problem, rather than ignoring that issue. Data scientists have a foundation in computer science, modeling, statistics, analytics, and mathematics, along with a strong business sense. They are often responsible for data management, analytics modeling, business analysis, and visualizing opportunities for business success.

A data scientist also requires to have knowledge of designing, developing, and deploying the most germane solutions for business and share their outcomes with stakeholders. Generally, he/she is someone who knows how to derive meaning from and interpret data.

Data Scientists work with some of the technological skills including programming skills in Java, Python, R, and SQL; Reporting and data visualization techniques; Big Data Hadoop and its various tools; Data mining for knowledge discovery and exploration; and communication and interpersonal skills.

Responsibilities:

As data scientists require to have academic, technological and business knowledge and skills, their job is to simply assess data for extracting actionable insights by:

• Identifying problems around data analytics to deliver the greatest value to organizations
• Determining the appropriate datasets and variables
• Gleaning structured and unstructured data from various sources
• Finding new solutions and opportunities by assessing data
• Cleaning and validating data to ensure accuracy, completeness, and uniformity
• Analyzing the data to identify patterns and trends
• Devising and applying models and algorithms for mining big data and much more

Become a Winning Data Scientist

The buzz around data science has significantly grown in recent times and will continue to rise. In order to become a winning data scientist, a candidate must be data-savvy. They must have the ability to not only influence enormous amounts of data with sophisticated statistical and visualization techniques, but have an obstinate acumen from which they can derive meaningful insights. While data science defines as a diverse field that demands programming knowledge along with an understanding of mathematics and statistics, an aspirant must comply with this knowledge and skills.

There are some degrees that are beneficial to become a data scientist include Applied Mathematics, Computer Science, Data Management, Economics, Information Technology, Mathematics, Physics, and Statistics.

Moreover, a majority of universities and institutes offer numerous certification programs in data science that can help a candidate to begin his/her career in this diverse field. Some of the leading data science certifications are Big Data Certification by UC San Diego Extension School; Data Science Certificate by Harvard Extension School; Data Science for Executives by Columbia University; Microsoft Certified Solutions Expert by Microsoft, among others.

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Artificial Intelligence continues to evolve through businesses across diverse industries, opening opportunities for organisations to operate and drive values to customers. From managing global supply chains to optimising delivery routes, the technology is augmenting its dominance in today’s digital world. This is why, AI is in high demand as businesses, be it small or large, are seeking to garner a competitive edge.

According to reports, demand for AI jobs continues to increase, and with more open jobs than qualified candidates to fill them, many AI-related roles rule high salaries. Similar to every country in the world, India is also racing to gain AI’s potential benefits that could be game-changing. In the country, AI is gaining rapid traction as large tech giants are looking at the country for new opportunities for their business growth.

Here are the top AI job titles with the highest pay in India.

Data Scientist

Data Scientists’ responsibilities revolve around Identifying valuable data sources and automating collection processes; Undertaking preprocessing of structured and unstructured data; Analyzing large amounts of information to discover trends and patterns, and more. They work closely with business stakeholders to understand their goals and determine how data can be used to accomplish those goals.

The average salary of a Data Scientist in India is INR 10,15,385 (US$13,332.26).

Data Engineer

Data engineers are the ones responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. They are often accountable for building algorithms to assist in providing easier access to raw data.

The average base pay of a Data Engineer is INR 8,25,676 (US$10,848.10).

Data Analyst

Data Analysts deliver value to their companies by taking insights about specific topics and then interprets, analyzes, and presents findings in comprehensive reports. They acquire data from primary or secondary data sources and maintain databases.

The average salary of a Data Analyst is INR 5,19,956 (US$6,831.42).

Algorithm Engineer

An Algorithm Engineer is responsible for developing an algorithm system that records all operations and can be maintained by the team, and managing design, development, and deployment of scalable, high volume and real-time system. He/she also need to research on algorithm improvements and implement data processing.

The average salary of an Algorithm Engineer in India is INR 7,21,192 (US$9,475.35)

Computer Vision Engineer

Computer vision engineers often apply computer vision research that is based on a large sum of data to solve real-world problems. They spend much of their time researching and implementing machine learning primitives and computer vision for their client companies. A Computer vision engineer has a significant amount of experience with a variety of systems, such as image recognition, machine learning and segmentation.

The national average salary of a Computer Vision Engineer is INR 4,17,549 (US$5,485.95).

Machine Learning Engineer

A Machine Learning Engineer is proficient in using data to training models, which are then used to automate processes such as image classification, speech recognition, and market forecasting. He/she works close to that of a data scientist as both roles perform with a large amount of information, and require excellent data management skills and the ability for complex modeling on dynamic data sets.

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Big data exploration usually starts at a high level of data abstraction, and then gradually plumbs into the depths of the data as companies learn more from it.

The approach has worked well, and is operative in many different types of applications.

For instance, GIS and mapping systems use data to visualize a big picture map and then to focus in on a specific point or location. As the data analyst drills down to this location, they can then look at other related data that might be appended to the location such as the demographics of individuals who live at that location, or the number of traffic accidents at that location.

However, there is also another ground up approach that has the ability to unlock hidden values of big data. This approach actually starts at the lowest level of the data and then works its way up to more sophisticated data structures to deliver data insights that are helpful to management and staff.

Here is an example:

“A single pixel display can reveal the visible color of a point, but also the infrared value, which can be used to measure vegetative health,” said Layton Hobbs, research and development director and vice president at Woolpert, an architecture, engineering and geospatial solutions firm.

Hobbs is talking about the potential of agriculture and forestry companies to go beyond basic geospatial data that they collect and unlock hidden treasures that are buried in geospatial data such as data on topography, soil, ground cover, plant health, and tree canopies.

“Most geospatial data is created for one specific reason or need, but there is so much more information in geospatial data that is underutilized or not recognized,” added Woolpert’s associate and geospatial discipline leader, Joe Cantz. “Particularly with the newer technologies, the data-rich information is growing exponentially, but we are using only a small percentage at this point.”

According to Woolpert officials, geospatial data pixels are capable of storing a much wider range of values than the traditional 256 values of an 8-bit image. “These modern systems often store four bands of data (red, green, blue and infrared) at up to 12 bits or around 4,000 values for each band,” said Hobbs. “Combining those four bands for image interpretation creates 256 trillion possible combinations at one spatial location! This is definitely overkill for most applications but shows the potential for big-data applications of imagery.”

Why does this matter for company big data projects?

IoT data, such as data captured and emitted by sensors, immediately comes to mind.

With IoT, you can start with your own top-down big data initiatives and analytics when it comes to utilizing data and imagery that gets sent from sensors on board drones—but what if you looked into each individual pixel of data that the drone was sending back—and discovered that there was additional data value captured that could answer questions that you weren’t interested in today, but could be in the future?

Here’s how you can optimize data for both current and future use:

Analyze what is possible to extract from a given unit of data (e.g., a pixel), even though you may not care about all of this information today.

This can be easily done. Referencing Layton Hobbs’ example, maybe you don’t care about the health of the forest floor today, but if you one day want to restore this forest after a harvest, understanding something about forest health will help. At that point, knowing everything you can obtain from your big data under management becomes significant.

Catalog the information capture that is possible at the lowest unit of big data.

If you are dealing with a pixel and you know that forest health and topography is possible to analyze from this data and you make a record of it, it is much easier to remember the information potential of your data and to activate it if and when you need to.

Don’t lose yourself in the details

While it is important to catalogue the information potential of your big data at the lowest level of data, it is equally important not to lose yourself in the details. If your job today is simply to map a forest and to identify stands of harvestable timber, stick with that. Don’t get off course with other types of data explorations that aren’t relevant to the task at hand.

Anticipating lessons learned

When I was running a marketing department for a bank, we used demographics for one of our checking campaigns by identifying persons in certain locations by age group, and then linking checking products to the various life cycle stages that customers were in. Later, we wanted to improve results, and we added occupation as well as age for targeting our checking products.

This is a common scenario for companies. They want to go back to the data to see if they can add more information so they can improve results.

By assessing and cataloguing the potential information yield of big data at the lowest level of the data, data analysts can be poised to open up the data to more comprehensive analytics that can unlock the answers to questions that the company will want to ask next.

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