Data Analytics
In today’s world, organisations generate a vast amount of data on a daily basis. However, without the proper tools and techniques to analyse and interpret this data, it becomes just a pile of meaningless information. This is where data analytics comes in - it helps organisations make sense of their data and gain valuable insights to improve their business.
Table of Contents
What is Data Analytics?
Who is a Data Analyst?
What Does a Data Analyst Do?
Why Start a Career as a Data Analyst?
Responsibilities of a Data Analyst
How to Become a Data Analyst: A Step-by-Step Guide Types of Data Analysts
Data Analyst Salary
Data Analyst Qualifications and Prerequisites
Bifurcation of Data Analyst Skills: Technical and Soft
What Is the Workplace/Work Environment of a Data Analyst Like?
Degrees Needed
Career Path and Progression or Job Outlook
Available Courses
Data Analyst FAQs
How Can EXcellenC Help You?
With the increase in data generation, the term "data analyst" has found its prominence today. However, to become a data analyst, there are a set of required data analyst skills and a few steps that have to be followed. In this article on how to become a data analyst, you will get an in-depth understanding of what you must do to become a data analyst, unearth the skills required to bag this position and you’ll get a step closer to your dream of becoming a data analyst.
To fulfil the responsibilities a data analyst must possess a vast and rich skillset:
1. Degree and Domain Expertise Add Image Related to Data Analytics
2. Knowledge of Programming
3. Knowledge of Data Analysis Tools
4. Understanding of Statistics and Machine Learning Algorithms
5. Knowledge of Data Visualization Tools
Before we discuss the above steps in detail, let us check out what is data analytics? Who is a data analyst? And what does a data analyst do, etc?
What is Data Analytics?
From a bird's eye view, the term “analyse” can mean scrutinizing something to derive useful information from it. Hence, data analytics can be defined as the process in which meaningful insights are drawn from raw data by scrutinizing it. Such insights can be your business information, product innovations, market trends, etc.
Data analytics can be compared to how you make a jigsaw puzzle. Your first step is to gather all the puzzle pieces and then fit them correctly to bring out the final picture. Similarly, in data analytics, you have to analyse data collected from several sources, clean it, and then transform it into information that humans can interpret.
The collected data can be structured, semi-structured, or unstructured in nature. The final result can be visually represented as graphs and charts which provide precise results of the analysis. Several tools and frameworks are used in the analysis process.
There is a high requirement for professionals who can help organizations convert raw data into meaningful information, which in turn can help the growth of the business. There are several job roles in the field of data analytics, and among the job pool, becoming a data analyst offers the most amazing career opportunities. So what’s next: understanding how to become a data analyst. And here’s how!
Who is a Data Analyst?
A data analyst collects, processes, and performs analysis of large datasets. Every business, be it small or big, generates, and collects data. This data can be in the form of customer feedback, accounts, logistics, marketing research, and so on.
A data analyst is a professional who takes this data and figures out numerous measures, such as how to improve customer experience, price new materials, and how to reduce transportation costs, to name a few. Data Analysts deal with data handling, data modeling, and reporting.
Once you know who is a data analyst, it's paramount to know the roles and responsibilities of a data analyst.
What Does a Data Analyst Do?
A data analyst is responsible for collecting, cleaning, and analysing data that can be used to improve business decisions. They must be able to effectively communicate their findings to those who will make the decisions. Data analysts typically have a strong background in mathematics and computer science.
The role of data analyst has become increasingly important as businesses have become more data-driven. Data analysts help businesses to make sense of the vast amounts of data that they collect. They use their skills in mathematics and computer science to clean and analyse data, and then communicate their findings to those who will make the decisions.
Why Start a Career as a Data Analyst?
The demand for data analysts is at an all-time high. According to the US Bureau of Labour Statistics, the employment of data analysts is projected to grow much faster than the average for all occupations in the coming years. It is due to the fact that as companies undergo digital transformation, they are generating large amounts of data that they may need to learn how to process, manage, or use properly. It is where data analysts provide valuable insights and interpretations of data that can benefit the business.
Starting a career as a data analyst can be an excellent choice for several reasons:
Growing Demand: With the increasing use of data in business decision-making, there is a growing demand for professionals with skills in data analysis. According to the US Bureau of Labour Statistics, the employment of data analysts is projected to grow much faster than the average for all occupations in the coming years.
High Earning Potential: Data analysts are often well-paid, with salaries varying depending on experience, industry, and location. Data analysts can often earn competitive salaries right out of college.
Variety of Opportunities: Data analysts can work in various industries, from healthcare and finance to marketing and retail. It means many different types of jobs and career paths are available for those with data analysis skills.
Challenging and Interesting Work: Data analysis can be difficult and rewarding, requiring problem-solving skills and attention to detail. Analysts are often tasked with finding insights in large and complex data sets, which can be exciting and intellectually stimulating.
Transferable Skills: The skills developed in data analysis, such as statistical analysis, data visualization, and data management, can be applied to various other fields and industries. It means that data analysts can build valuable transferable skills that can be useful throughout their careers.
Responsibilities of a Data Analyst:
The data analyst designation comes with a bunch of roles and responsibilities. And the first step to becoming a data analyst in understanding the responsibilities of one! Some of the common and expected responsibilities of a data analyst are:
1. Understanding the Goal
First and foremost, a data analyst must identify the organization’s goal. They must assess the available resources, comprehend the business problem, and collect the right data.
2. Querying
Data analysts write complex SQL queries and scripts to gather, store, manipulate, and retrieve information from relational databases such as MS SQL Server, Oracle DB, and MySQL.
3. Data Mining
Data is mined from a plethora of sources and organized to obtain new details from it. By doing so, data models are built to increase the efficiency of the system.
4. Data Cleansing
Cleaning and data wrangling is the vital duties of a data analyst. The data gathered initially will often be messy and have missing values. Hence, it’s crucial to clean the collected data to make it ready for the analysis purpose.
5. Data Examining
Data analysts use analytical and statistical tools, including programming languages, for carrying out a logical examination of data.
6. Interpreting Data Trends
Data analysts use various packages and libraries to spot trends and patterns from complex datasets, thereby discovering unseen business insights.
7. Preparing Summary Reports
Data analysts prepare summary reports with the help of data visualization tools. These reports guide the leadership team to make timely decisions.
8. Collaborating with Other Teams
Data analysts interact with the management team, development team, and data scientists to ensure proper implementation of business requirements and figure out process improvement opportunities.
And now that you’re well aware of the roles and responsibilities of a data analyst, you are ready to unlock the secret - how to become a data analyst.
How to Become a Data Analyst: A Step-by-Step Guide
Becoming a data analyst can be rewarding, with high earning potential and a growing demand for skilled professionals. Here's a step-by-step guide on how to become a data analyst:
Learn the Essential Data Analysis Skills
When embarking on a journey to become a Data Analyst, it's essential to start by solidifying the fundamental principles of data analysis. You must also become proficient in various data analysis tools, such as advanced Microsoft Excel, programming languages like SQL, Python, and R, and Spark and Hadoop for complex calculations. Additionally, tools like ggplot2, Tableau, or Matplotlib can help you create stunning visualizations that can help bring your data to life.
One of the best ways to gain proficiency in these skills is through taking data analysis courses. These courses offer an excellent opportunity to learn the basics of data analysis and gain hands-on experience working with data on these platforms. It will give you a solid foundation to build your skills as you move forward.
As you progress, focusing on developing practical experience by working on realworld projects is crucial. It will allow you to put your knowledge into practice and learn how to apply the concepts you've learned to actual data. This experience will help you build a strong portfolio of work that will demonstrate your abilities to potential employers.
Additionally, networking is critical in any profession, and data analysis is no exception. Joining data analysis communities, attending meetups, and participating in hackathons can help you meet other professionals in the field and learn from their experience. It can open doors to new opportunities and help you stay up-to-date with the latest developments in the industry.
Practice Your Data Analyst Skills on a Variety of Topics:
If you're interested in becoming a Data Analyst, it's essential first to thoroughly understand the basic tools and skills required for the job. Once you fully grasp these fundamentals, you can apply your knowledge to real-world projects. As a successful Data Analyst, it's also essential to stay up-to-date with the latest tools and technologies used in the field.
Some essential tools and skills required for data analysis include spreadsheets and querying languages like XML and SQL, programming languages and frameworks like Hadoop, Python, and R, and visualization tools like PowerBI, Tableau, Matplotlib, Plotly, and Bokeh. In addition, experience with leading data analysis platforms such as Google Analytics and Adobe Analytics can be precious.
To gain practical experience, it's essential to work on projects that cover all stages of data analysis, including researching companies and market opportunities, identifying and collecting relevant data, cleaning and pre-processing that data, and modeling and analysing it using custom-built algorithms. Once you have derived insights from your analysis, it's crucial to present them easily through beautiful visualizations or interactive dashboards.
Working on a range of practice projects that allow you to work with different data types is essential to develop further and showcase your abilities as a Data Analyst. It may include mining structured data, working with text and images, analysing audio, and processing video data to make predictions and identify causality.
By taking on these diverse projects, you'll be able to build and demonstrate your skills in statistical analysis, data modeling, and machine learning, all of which are crucial to success as a Data Analyst. In addition, working on various projects will help you develop the soft skills that set top-performing Data Analysts apart from their peers.
Practice Presenting Your Findings
To become a successful Data Analyst, it's vital to develop proficiency in programs like Tableau, PowerBI, Bokeh, Plotly, or Infogram. As you begin to use these tools, it's essential to build your visualizations from scratch and find the best ways to communicate insights from the data.
In addition to these tools, Microsoft Excel remains a critical component of the Data Analyst's toolkit. Although Excel has existed for over 30 years, it remains a fundamental data analysis and visualization tool.
However, creating visualizations is just one part of the job. To be an effective Data Analyst, you must also be skilled at communicating your findings to others. It requires strong presentation skills, which may only come naturally to some. If you need to improve your presentation skills, start practicing with small groups of friends or colleagues before moving on to larger audiences.
By focusing on technical proficiency and communication skills, you'll be wellequipped to excel as a Data Analyst and contribute to your organization or team.
Create a Portfolio of Your Work
When planning to become a Data Analyst, it's essential to consider how you will showcase your data skills and knowledge to potential employers. Building a professional portfolio is a crucial step, and one way to get started is by sharing the code you've written on platforms like GitHub. It lets you showcase your technical abilities and build a work collection demonstrating your expertise.
When selecting data projects for your portfolio, choosing something that genuinely interests you is important. It will make the project more engaging and motivate you to complete it. Once you've selected a topic, formulate a question that can be answered with data and use your analytical skills to find the answer.
Successfully executing a data project is a great way to demonstrate your data analysis abilities and make a strong impression on potential employers. By building a robust portfolio of projects, you'll be able to showcase your skills and increase your chances of landing your dream job as a Data Analyst.
Apply for Entry-Level Data Analyst Jobs
The world of data analytics offers diverse job opportunities for those with data skills. Some popular roles that require data analysis expertise include Data Analyst, Business Analyst, Data Scientist, Data Engineer, Data Architect, and Marketing Analyst.
However, the field of data analytics is constantly evolving, and new applications for data analysis are being developed all the time. As a result, job titles and responsibilities may also continue to change and expand.
Given the high demand for data professionals, consulting firm McKinsey & Co. predicts that demand may outpace the projected supply of data professionals by 50 or 60 per cent in the coming years. Those with solid data analysis skills will have many opportunities to build a successful career in this field.
To increase your chances of success, staying up-to-date with the latest data analysis tools and technologies and continually developing your skills through practice projects and real-world experience is essential. By doing so, you can build a strong portfolio of work and position yourself as a highly skilled and sought-after data professional.
Types of Data Analysts
Depending on your interests and skill set, you can pursue several types of Data Analyst roles. Some common types of Data Analysts include:
Business/Data Analyst
A Business/Data Analyst is responsible for collecting, analysing, and interpreting complex data sets to help companies make informed decisions. They work closely with stakeholders to identify business requirements and design supporting data models. They may also develop reports and dashboards to present data insights to decision-makers.
Marketing Analyst
A Marketing Analyst uses data to help companies understand their customers and develop marketing strategies. They analyse customer behaviour, demographic data, and market trends to help companies effectively target their marketing efforts. They may also build marketing performance metrics to track the success of marketing campaigns.
Financial Analyst:
A Financial Analyst uses data to help companies make financial decisions. They may analyse financial data such as revenue, expenses, and profitability to help companies identify areas for improvement or growth. They may also develop economic models to forecast future performance and inform strategic planning.
Healthcare Analyst:
A Healthcare Analyst uses data to help healthcare organizations improve patient outcomes and reduce costs. They may analyse healthcare data such as patient records, clinical trials, and insurance claims to identify trends and patterns. They may also develop predictive models to help healthcare providers make more informed decisions.
Data Scientist:
A Data Scientist is responsible for designing and developing complex algorithms and models to solve data-driven problems. They work with large, complex data sets and use advanced analytical techniques to extract insights and develop predictive models. They may also work with other Data Analysts to develop data-driven solutions for businesses.
Data Analyst Salary
How to become a data analyst is the real question, however, there’s one motivation that will surely push you forward to becoming one - and that salaries potential!
According to Glassdoor, a data analyst in the United States can earn a minimum salary of $43K to a maximum of $95K per year.
In India, a data analyst can earn an average salary of 500K per annum.
The average salary of a data analyst in Australia is around A$83K per annum.
In the United Kingdom, a data analyst earns an average salary of £30K per annum.
Data analysts have a lot of scope in today's times, as companies are on the lookout for professionals who can efficiently and effectively handle their data.
The companies hiring for the position of data analysts are Amazon, Microsoft, Capital One, Walmart, PayPal, Google, Facebook, Twitter, Bloomberg, and Apple, to name a few.
Are you overwhelmed with all the information and cannot figure where and how to start your learning journey? We have your back. Here’s how EXcellenC can help you learn Data Analytics and make you a seasoned Data Analyst.
Data Analyst Qualifications and Prerequisites
There are a few key qualifications and prerequisites that data analysts should possess in order to be successful in their role. Firstly, data analysts should have strong mathematical skills and be able to effectively analyse data sets. Secondly, they should be well-versed in using statistical software packages such as SAS, R, or SPSS. Finally, data analysts should also have strong communication skills in order to be able to effectively present their findings to clients or other stakeholders.
Possessing these qualifications and prerequisites will put data analysts in a good position to excel in their roles and provide valuable insights that can help organizations make better decisions.
Technical Skills
Data Wrangling: Collecting and processing large volumes of data from various sources. Data Cleaning: Scrubbing data of errors, inconsistencies, and inaccuracies to ensure accurate analysis.
Data Analysis: Analysing data using statistical methods and tools like SQL, Python, R, Excel, or Tableau.
Data Visualization: Creating charts, graphs, and other visual representations of data to communicate insights effectively.
Data Modeling: Building mathematical models that represent real-world phenomena and help predict future outcomes.
Data Mining: Identifying patterns and insights from large datasets.
Machine Learning: Applying algorithms and statistical models enables machines to learn from data and make predictions.
Soft Skills
Communication: Being able to communicate insights, analyses, and findings to a variety of stakeholders.
Problem-Solving: Being able to approach problems methodically, identify root causes, and develop practical solutions.
Attention to Detail: Being meticulous in data cleaning, analysis, and modeling to ensure accuracy and reliability.
Critical Thinking: Evaluating information, identifying patterns and trends, and drawing meaningful conclusions.
Time Management: Prioritising tasks and managing multiple projects and deadlines effectively.
Teamwork: Being able to collaborate with others and work towards common goals.
Adaptability: Adapting to new tools, technologies, and approaches as the field of data analytics evolves.
What Is the Workplace/Work Environment of a Data Analyst Like?
A Data Analyst's workplace and work environment can vary depending on their industry and company. Data Analysts may work in a range of settings, including traditional office environments, remote work, or a combination of both.
Data Analysts may work independently or as part of a team, collaborating with other data professionals such as Data Scientists, Business Intelligence Analysts, or Data Engineers. They may also work closely with other departments, such as marketing, finance, or operations, to provide insights and recommendations.
Data Analysts typically spend a lot of their time analysing and manipulating data using various tools and software. They may also be responsible for data cleaning and preparation, building and testing models, and creating data visualizations or reports to communicate their findings.
In some cases, Data Analysts may be required to work with large datasets that require specialized hardware or cloud-based computing resources. In such cases, they may work closely with IT professionals to set up and manage these resources.
In short, the work environment of a Data Analyst is typically fast-paced, detailoriented, and focused on problem-solving. Data Analysts are expected to be selfmotivated, highly analytical, and able to communicate their findings effectively to stakeholders.
Degrees Needed
There is a common misconception that in order to become a data analyst, you need to have a degree in mathematics or statistics. However, this is simply not the case. While having a background in these subjects can certainly be helpful, it is by no means essential.
Many data analysts actually come from non-technical backgrounds. What they do have in common is a strong interest in working with data and an aptitude for solving problems. If you have these qualities, then you may well be suited to a career in data analysis – regardless of your educational background.
Of course, if you do have a degree in mathematics or statistics, this can give you a significant advantage when it comes to understanding complex data sets. However, it is important to remember that data analysis is more than just number crunching. It is also about being able to communicate your findings in a clear and concise way. So even if you don't have a background in mathematics or statistics, if you are good at communicating your ideas, you can still be a good candidate for a data analyst role.
While a degree is not always a requirement for a data analyst role, earning the relevant certification from an accredited institution like EXcellenC is imperative.
Career Path and Progression or Job Outlook
Data analysis is a rapidly growing field with solid job prospects for the future. According to the United States Bureau of Labor Statistics, employment of computer and information research scientists (which includes data analysts) is projected to grow 15% from 2019 to 2029, much faster than the average for all occupations.
Data analysts can expect to start their careers in entry-level positions, such as data analyst or junior data scientist, and progress to mid-level positions, such as senior data analyst, data scientist, or data engineer. With experience and further education, data analysts can advance to leadership roles such as data analytics manager, business intelligence manager, or chief data officer.
In addition to traditional career paths within a single company, data analysts can work as consultants or freelancers, providing their expertise to multiple clients. The rise of big data and the need for data-driven decision-making in all industries means that the demand for data analysts will likely continue to grow in the years to come.
Available Courses
There are many different types of courses available to help you become a data analyst. Some of these courses are offered by colleges and universities, while others are offered online.
Undergraduate Courses
There are a number of undergraduate courses that can help you become a data analyst. A few of the most popular include:
Data Analytics
Computer Science
Information Systems
Statistics
These courses will provide you with the skills and knowledge necessary to work as a data analyst. In addition, it is important to have strong problem solving and critical thinking skills. Data analysts must be able to understand complex data sets and find ways to extract meaning from them. They must also be able to communicate their findings to others in a clear and concise manner.
Postgraduate Courses
There are a few different types of postgraduate courses that can help you become a data analyst. A Master's in Data Science is one option, and this type of course will teach you the basics of data analysis and give you the opportunity to put your skills into practice on real-world projects. Alternatively, you could study for a Master's in Business Analytics, which will give you a more business-focused perspective on data analysis. Whichever route you choose, make sure you choose a course that offers plenty of hands-on experience so that you can graduate with the skills and knowledge you need to succeed in this exciting field.
Another option is to enrol in our Advanced Certification Course in Data Analytics that solely focuses on all aspects of data analysis, which can help you learn what you need to succeed in this thriving industry in just a fraction of the time, and for a fraction of the cost.
Data Analyst FAQs
1. What does a data analyst do?
A: Data analysts collect, organize, and analyse data to help businesses make better decisions. They use their skills in math and statistics to find trends and insights in data sets, then communicate their findings to decision-makers.
2. What skills do I need to be a data analyst?
A: To be a successful data analyst, you need strong math and analytical skills. You must be able to think logically and solve problems, and have attention to detail. Additionally, you must be able to effectively communicate your findings to those who will make decisions based on your analysis.
3. What education do I need to be a data analyst?
A: Some data analysts have a bachelor’s degree in math, statistics, economics, computer science, or another quantitative field. However, with the right skills and experience, it is possible to become a data analyst without a college education in this area. More employers are also favouring candidates who have earned certifications with a sole focus in data analysis.
4. What are some common software programs used by data analysts?
A: Data analysts commonly use spreadsheet programs such as Microsoft Excel or Google Sheets to organize and analyse data. They may also use statistical analysis software, such as SAS or SPSS, to conduct more complex analyses. Additionally, data analysts may use database management software, such as MySQL or Oracle, to work with large data sets.
5. What are some common challenges faced by data analysts?
A: Data analysts may face several challenges in their work. First, they may need to clean or “wrangle” data sets that are messy or incomplete. Second, they may need to find trends or insights in large data sets, which can be difficult and timeconsuming. Third, they may need to effectively communicate their findings to those who make decisions based on their analysis. Finally, data analysts may need to stay up-to-date on new analytical methods and software programs.
6. How long does it take to become a data analyst?
The time it takes to become a data analyst can vary depending on your prior experience and education, the amount of time you can commit to learning, and the specific skills and knowledge you need to acquire. A bachelor's degree in a related field like statistics, computer science, or mathematics is required to become a data analyst. Obtaining a bachelor's degree can take around four years of full-time study.
However, learning the necessary skills through self-study or a course from training institute is also possible. These programs can range from a few weeks to several months, depending on the intensity and duration of the program. Once you have the necessary education or skills, it may take some time to build up experience in the field before securing a full-time data analyst position.
7. What tools do data analysts use?
Data analysts use a wide range of tools to analyse and manipulate data. Here are some of the most common tools used by data analysts:
Microsoft Excel
SQL
Python
R
Tableau
Power BI
Hadoop
Spark
SAS
MATLAB
Google Analytics
Adobe Analytics
8. Is data analysis a good career?
Yes, data analysis is generally considered a promising career with solid job prospects and competitive salaries. The demand for data analysts is growing rapidly as more and more companies realize the importance of data-driven decision-making. With the right skills and experience, data analysts can advance to more senior roles such as data scientist or analytics manager. Additionally, the skills required for a data analyst role are transferable across industries, allowing for a range of career opportunities.
9. Can anyone become a data analyst?
Having a background in computer science, mathematics, statistics, or related fields may make it easier to become a data analyst, but anyone with the right skills and determination can become a data analyst. The key is to gain proficiency in the necessary technical skills like programming languages (such as Python, R, and SQL), data visualization tools (like Tableau and PowerBI), database management tools, and the ability to analyse and draw insights from data.
Soft skills like communication, problem-solving, and critical thinking are also essential. While formal education in data analysis or a related field can be helpful, many online resources, courses, and boot camps are available for anyone interested in learning data analysis. With dedication and a willingness to learn, anyone can become a data analyst.
10. Does data analytics require coding?
Yes, data analytics often requires coding skills. While some data analysis tools allow for visual manipulation of data without coding’s, such as Tableau, Power BI, or Excel, proficiency in programming languages like Python, R, SQL, and Java can be highly beneficial in performing advanced analysis and building custom data models. In addition, knowledge of programming and scripting languages can help automate repetitive tasks and streamline data analysis processes.
11. Is data analysis an IT job?
Data analysis is not necessarily an IT (information technology) job but requires working with IT tools and systems. Data analysis involves using statistical and computational techniques to derive insights from data, which can be applied in various industries such as healthcare, finance, marketing, and more. While data analysts need to have a basic understanding of IT tools, such as programming languages and database systems, their primary focus is on analysing data and interpreting results to drive business decisions.
12. What is the difference between a data analyst and a data scientist?
Data analysts and data scientists are both data professionals, but there are some critical differences in their roles, skills, and responsibilities:
Role: Data analysts primarily work with structured data to derive insights and make data-driven decisions. Data scientists use structured and unstructured data to develop and implement predictive models and algorithms.
Skills: Data analysts require strong skills in data management, statistical analysis, data visualization, and business domain knowledge. Data scientists need advanced machine learning, programming, and data engineering skills.
Responsibilities: Data analysts typically focus on data cleaning, data analysis, and reporting, while data scientists focus on developing and deploying predictive models and algorithms that can drive business outcomes.
13. Can I become a data analyst without a degree?
Yes, it is possible to become a data analyst without a degree. While having a degree in a related field, such as computer science, statistics, or mathematics, may be preferred by some employers, it is only sometimes a requirement.
To become a data analyst without a degree, you can gain the necessary skills through self-learning, online courses, boot camps, or relevant certifications. You can learn programming languages like Python or R, SQL, and data visualization tools like Tableau or PowerBI.
Creating a strong portfolio of personal projects and participating in online communities or hackathons can also help demonstrate your skills to potential employers. Additionally, networking and reaching out to professionals in the field can help you learn more about the industry and increase your chances of finding job opportunities.
How Can EXcellenC Help You?
If you're looking to become a data analyst, then our Advanced Certification Course in Data Analytics, in collaboration with Texas Tech University is a great choice. The course will cover Introduction to Data Analytics, Excel, SQL, Statistics, Tableau, and Power BI. You will also learn Programming Basics and Data Analytics with Python, R programming, and finally, you will get to work on a real-time live project.
This program will help you become a data analyst pro. And now you know the best way to become a data analyst seamlessly. The tools covered in this program are Excel, NumPy, Pandas, SciPy, Power BI, Tableau, Python, and R.
Now that you know what data analytics is, who a data analyst is, the responsibilities of a data analyst, and how to become a data analyst, you’re all set to start your learning journey and prepare for a data analyst interview. So what are you waiting for? Your dream job role is a click away. Start learning now!
In case you have any questions on this article ‘How To become a Data Analyst’? mention them in the comment section below Our experts will get back to you as soon!
About the Author
Chethan V S
Data Scientist, Business Analyst and Director
Analytics Space
