The phrase "data analyst" has gained popularity in recent years due to the increased creation of data. However, there are a few requirements and processes you must do to become a data analyst. You will learn just what you need to do to become a data analyst in this blog, learn the skills needed to land this job, and move one step closer to acknowledging your ambition of being a data analyst.
From a bird's eye, "analyze" can mean examining something to get helpful information. Thus, data analytics can be defined as extracting meaningful insights from raw data by observing it. These insights could include knowledge about your company, new products, market trends, etc. So let's explore, how to get started in data analytics.
What is Data Analytics?
Data analytics is comparable to putting together a jigsaw puzzle. Your first step is to collect all the puzzle pieces and then put them together correctly to get the final picture. Similarly, in data analytics, you need to analyze data collected from multiple sources, clean it, and transform it into information people can interpret.
The data collected may be structured, semi-structured, or unstructured. Tables and graphs that show the final result with accuracy from the analysis can be used as visual representations. Professionals who can assist firms in turning raw data into knowledge that is useful and, in turn, can drive corporate success, are in great demand. A career as a data analyst offers the most amazing employment options among the various positions in the field of data analysis.
Types of Data Analytics
In data analysis, there are various methods of obtaining the information you need to gain the insights, patterns, and trends that drive business decisions. Data analytics primarily focus on:
1. Descriptive Analytics
As the name suggests, this analysis describes what happened and presents it in a digestible picture. Descriptive data analysis uses data aggregation and mining to provide an overview of past actions, often the starting point for more profound research.
2. Diagnostic Analytics
The difference between descriptive and diagnostic analysis is that while descriptive analysis tries to provide an objective overview of what happened, the diagnostic analysis aims to find out why those things might have happened. This can be done by identifying and managing outliers or anomalies in your data.
What Skills Do You Need To Become A Data Analyst?
Getting started in data analytics you need essential tips, tools, and insights. There are some traditional hard and soft skills that any data analyst entering the field will need. This list is not exhaustive, but consider it a starting point if you are considering a career change.
Hard Skills Required to Become a Data Analyst
- Demonstrated knowledge of programming and query languages such as Python and SQL
- Shown proficiency in business intelligence and data analytics tools, such as SAS, Tableau, and RapidMiner
- Having a thorough understanding of each stage involved in data analysis
- strong mathematical and statistical abilities
As you may have already discovered, hard skills are the technical skills required to meet the role's requirements. They are generally measurable in terms of expertise from basic skills to advanced expertise.
Soft Skills Needed To Become a Data Analyst
- Excellent cooperation and communication skills
- Eye for detail
- A methodical and logical approach
- Thinking about solving problems
Soft skills are not measurable compared to hard skills. Think of soft skills more as traits that are part of your existing personality, even if you may have acquired or honed those skills through other roles or experiences.
Data Analyst Responsibilities
The
data analyst designation comes with a variety of roles and responsibilities. Some of the common and expected duties of a data analyst are:
1. Understanding Objectives
First and foremost, the data analyst must determine the organization's goal. They must assess available resources, understand the business problem, and gather the correct data.
2. Questioning
Data analysts create intricate SQL queries and scripts to gather, store, manage, and retrieve data from relational databases like MS SQL Server, Oracle DB, and MySQL.
3. Data Mining
Data is mined from multiple sources and organized to extract new details. This way, data models have been created that increase the system's efficiency.
4. Data Cleaning
Cleaning and wrangling data are vital duties of a data analyst. Data collected at the beginning will often be messy and have missing values. Therefore, cleaning the collected data is crucial to preparing it for analysis.
5. Examining Data
Data analysts utilize analytical and statistical tools, including programming languages, to logically examine data.
6. Interpretation of Data Trends
Data analysts use a variety of packages and libraries to recognize trends and patterns from complex datasets, uncovering unseen business insights.
7. Preparation of Summary Reports
Data analysts prepare synopsis reports with the use of data visualization tools. These reports guide the management team in making timely decisions.
8. Collaboration With Other Teams
Data analysts work with the management team, development team, and data scientists to ensure proper implementation of business requirements and identify opportunities for process improvement.
3 Steps To Become a Data Analyst
Becoming the best data analyst requires a combination of technical skills, an analytical mindset, and effective communication abilities. You don't need a degree in these fields, although getting one can enhance your resume and deepen your knowledge. Here are some tips to help you on your journey to becoming a top-notch data analyst:
1. Learn the Basics
- Develop a strong foundation in statistics, mathematics, and programming (commonly using languages like Python or R).
- Gain proficiency in data manipulation and analysis libraries such as Pandas, NumPy, and sci-kit-learn (for Python).
2. Learn Technical Skills
Success in a data analyst role requires a wide range of technical skills. These include knowledge of hypothesis testing, probability distributions, and regression analysis. For instance, by taking classes and working with unprocessed datasets, you can obtain practical SQL skills. It would be beneficial to become familiar with data preparation and cleaning procedures and learn how to use Microsoft Excel, MATLAB, and IBM SPSS with various data sets.
You can enroll in degree programs or online
data analyst course that offer practical projects and datasets for building models. As an alternative, you can plan your initiatives and conduct your analysis utilizing cost-free datasets from public enterprises or government organizations. There is a lot of information, and you choose the one that interests you the most. Public dataset examples to get you started include:
- Search Google Datasets
- Data.gov
- Earth data
3. Build a Solid Portfolio of Personal Projects
Making a strong portfolio of projects that showcase your knowledge in the subject is the next stage in establishing a successful career in data analytics. As soon as you master the fundamentals, you may have worked with a few clients, but your best personal projects will frequently be more important in your job search. Employers looking to hire you want to know that you have the qualifications to contribute to their businesses. As a result, you should spend time managing a portfolio that demonstrates your ability to:
- Collect raw data
- Clean and prepare raw data
- Present data using maps, tables, and graphs
- Obtain useful knowledge from the processed data.
Additionally, you want to think about including group projects in your portfolio to highlight your collaboration skills. However, when building an online portfolio, you must prevent errors.
Remember that becoming an excellent data analyst is an ongoing process of learning and refining your skills. Stay curious, be proactive in seeking new challenges, and continuously seek opportunities for growth. After acquiring all these steps you can now start applying for entry-level data analyst jobs.
Our Learners Also Read: What are Data Scientist Top Skills?
Data Analyst Job Salary
The salary can vary depending on factors such as location, level of experience, industry, and the specific company.
Entry-level Data Analysts with little to no experience might earn an annual salary ranging from
₹3 lakh to ₹6 lakh. With a few years of experience, mid-level Data Analysts could earn between
₹6 lakh to ₹12 lakh per year. Senior Data Analysts or those with extensive experience may earn salaries exceeding
₹12 lakh to ₹20 lakh or more.
Conclusion
In summary, the path to becoming a data analyst involves mastering technical skills, continuous learning, and effective communication. With competitive salaries and abundant job opportunities, this dynamic field rewards those passionate about turning data into actionable insights. As the world becomes more data-centric, data analysts play a crucial role. Whether you're a seasoned professional or a newcomer, embracing the challenges of data analytics opens doors to a fulfilling and lucrative career in this ever-evolving landscape.
