Proven Tips To Build A Data Analyst Career For Beginners
Big data analytics is one of the hottest career fields today. If you’re thinking of pursuing a career in data analytics, this guide will tell you everything you need to know.
A career in data analytics is exceptional, by dint of increased data in divergent businesses and industries. Several companies, governments, and people take on board the power of data, and job prospects in this field are only improving. Every organization is looking for atypical data analysts that are proficient with data, such as to transform their unprocessed datasets into insightful business comprahensions and help them make informed decisions.
The exigency of data analytics in 2022
The process of gathering a sizable amount of data and processing it to look for commonalities is known as data analytics. In order to stock the popular things on customers’ wishlists, businesses use this procedure to identify and streamline the products that are on racks. Businesses develop consumer profiles and identify trends by studying this data. For example, data analytics at intervals determine the average amount of time spent shopping, whether it be in-store or online, and identify which demographic groups are more likely to make larger purchases (or less).
Therefore, those wishing to begin a career in data analytics have plenty of opportunities, thanks to the rising demand and excellent salaries. An entry-level salary of a data analyst ranges between USD 87,171 to USD 99,459 (Indeed reports).
- Is a career in data analytics promising?
- What are the to-do lists that I need to cover as an endeavoring data analyst?
- How about earning a data analyst certification?
- How about an undergraduate followed by a role in data analytics?
All of these will be answered in this article, that said, let us begin by understanding the steps involved in analyzing data.
👉 The important five for data analysis
The techniques needed to make sense of the data that is currently available make up the data analysis process. It should be obvious how to identify the crucial steps in a process of data analysis. To make sure that the data is properly evaluated and produces useful and actionable information, each step is equally crucial. You can consider these steps as a typical day for a data analyst.
Let’s examine the five crucial actions that comprise a data evaluation process flow.
1) Defining the need for analysis
Finding out why data analysis is necessary — whether it’s to decrease production costs, boost sales opportunities, or assess the reputation of a brand — is the first stage in a data analysis process.
2) Gathering data
Once a goal has been established, it is time to start gathering the data required for analysis. Data can be structured or unstructured, often gathered using marketing automation tools, ERP systems, and CRM software.
3) Clearing the unnecessary data
Your data team will be in charge of cleaning and organizing the data once it has been gathered from all required sources. For the simple reason that not all data is good data, data cleaning is crucial during the analysis process. You can use intelligent automation tools like Tableau, Weka, SAS, and Pentaho to save time analyzing data.
4) Perform the analysis
Manipulating and evaluating data can be accomplished in different ways such as data mining to unveil hidden data patterns. Data visualization and business intelligence software can also be brought into play to have digestible charts, dashboards, and reports.
5) Elucidate the outcome
The results of your data analysis should, even if they are not conclusive, support the objectives you set out to achieve. Multiple options can be investigated and tried to reduce production costs without sacrificing quality.
👉 Important programming tools used to analyze data
Although the aforementioned processes appear to be very simple in theory, they demand programming knowledge and practical programming skills. The positive scoop is that there are several data analysis programming languages available to aid you. These consist of:
1) Python
Python for data analysis is an ideal programming language. It is flexible, object-oriented, simple to read, and effective for data analysis. It can be used to build web pages, code programs, and extract information.
2) Java
Java, a general-purpose language, provides strong tools for incorporating data science and analytical techniques into a codebase. At present, the Java backend is used to build many contemporary systems. The use of this language in data applications is crucial.
Java allows for smooth platform portability and enables it to create specialized production codes and computationally demanding ML algorithms. It is ideal for specialized statistical applications and ad hoc studies,
3) R
Simple to learn language R does not need as many additional libraries as Python does, and lets you look for patterns in your data. It can be employed to create spectacular data visualizations or statistical models.
R offers statistical packages for quantitative applications, which is why data analysts bargain on it. The language consists of nonlinear regression, phylogenetics, neural networks, advanced charting, and another open-source language made to be flexible.
4) SQL
Given that it was created with a specific goal in mind, SQL for data analysis is a robust scripting language with a straightforward syntax that is fairly simple to learn and that enables you to interact with relational databases, perform searches inside them, and gather data for use. SQL can be used to evaluate business data since it is effective at manipulating data.
5) Scala
Programming in Scala combines functional and object-oriented principles. Many data analysts, especially those who work with huge volume data sets, prefer to use the multi-paradigm language because it operates on JVM. The cluster computing framework, Apache Spark, functions well with Scala. This makes working with huge data collections simple.
👉 Checklist to successfully ace a data analyst career for beginners
Data analytics jobs pertain across all industries. There are multiple ways available toward securing your first job role in the actively hiring field. The following are some of the finest and most reliable steps that you must follow to get a good hold as a big data analyst.
1) Begin by understanding the fundamentals of data analysis
If you are new to data analysis, you should begin by learning the basics of the subject. You can assess whether this career is a suitable fit for you by gaining a wide understanding of data analytics and developing job-ready abilities.
Additionally, you might understand what you require by reading books, journals, and related articles. Some of the top books recommended to read are:
- Mathematical Foundations of Big Data Analytics
- Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting data
- Big Data Analytics: Systems, Algorithms, Applications
- Practical Big Data Analytics
If you prefer video tutorials, you can watch them on YouTube that explains big data analysis and what a big data analyst does on a typical day.
2) Build your technical and non-technical data analytics skills
Having a particular set of technical skills is often necessary to land a job in data analysis. These are some fundamental abilities you will probably need to get hired, whether you are learning through a degree program, a data analyst certification, or on your own. As explained in the aforementioned section skills like Python, Scala, R, Java, and C are extremely essential if you are keen on crunching numbers and potentially look forward to handling roles that involve analysis and research of data, or managing information systems in companies.
Please note that a lack of interpersonal skills may end up being a deal breaker. Employing managers also cast around workplace competencies including strong communication skills (you may be required to communicate your results to individuals who lack as much technical knowledge), problem-solving prowess, and industry-specific expertise. Learning to communicate consistently is a vital tool for developing healthy relationships.
3) Earn big data analytics certifications
The majority of entry-level jobs for data analysts in the past required a bachelor’s degree. Although many jobs still require a degree, this is starting to change. A degree in computer science, math, or a related subject can help you gain the fundamental understanding you need, but you can also do the spadework you need by earning a big data analyst certification. Here is some that top the beginner certification list.
- Data Analyst Professional Certificate by IBM
- Associate Big Data Analyst (ABDA™) Certification by DASCA
- Data Analyst with Python by DataCamp
- Data Analytics Professional Certificate by Google.
4) Work on big data projects
If you are considering becoming a big data analyst, make sure you don’t chain yourself to a single degree for the rest of your life, when there are a million choices out there that could help you attain your career goals and use your data analyst certification skills.
Tip: Try to complete smaller data analysis projects based on your skills as a beginner.
Some projects that you can work on are:
📍Web scraping: An excellent way to automate repetitive data gathering methods such as pulling PDs, reviews, and other data from e-commerce sites. This can be done using Python, Github, and other web scraping tools.
📍Exploratory Data Analysis: This helps identify trends and patterns within data, providing you with real insights.
📍Pollution in the U.S: This data can be visualized using R and Python. You can model changes in concentrations, and emissions according to time, month, or day of the week.
5) Build your portfolio
You should keep your best work for your portfolio as you experiment with online data sets or finish practical projects. Hiring managers can see your skills in a portfolio. Having a solid portfolio can help you land a job. The following is an example of what a data analyst portfolio should look like:
Anubhav Gupta is a data analyst whose portfolio outstands others. The portfolio is well-structured, compact, and has supporting information on one short home page. This approach will not let users or scrollers lose track of where they are at. Similarly, your portfolio should be:
- Clear without any fussy headline
- Giving brief introduction about your interests, personality, and experience, leaving the rest to be looked for in your resume
- Having catchy headlines for your projects, details of your case studies must be clear and precise.
As a data analyst, you must remember that a little humility may go a long way and that sometimes less is more when it comes to your professional portfolio. Including a limited number of tasks rather than including everything often indicates better confidence.
Tip: You can build your portfolio on Weebly, Wix, Fabrik, and GitHub.
👉 Apply for entry-level posts to develop experience
It’s time to upgrade your resume and start applying for entry-level data analyst positions after you have gained some experience using data and communicating your conclusions. It is important not to be intimidated to apply for jobs, for which you may not feel quite qualified. When applying for a job, your talents, portfolio, data analyst certification, and excitement typically matter more than whether you can check off every bullet point on the requirements list.
In a nutshell…
Pursuing a career in data analytics is worth all the time, cost, and effort. As a data analytics specialist, you frequently deal with difficult issues at work, which motivates you to come up with a methodical, algorithmic solution and makes you a solution-focused person. Professionals in this sector not only earn well but also are held in high regard.
Therefore, do not wait any longer if you are interested in pursuing a profession in data analytics. The time is right to seize the chance to pursue a career in this field.
