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How to Start a Career in Data Analytics [Step-by-Step Guide]

How to Start a Career in Data Analytics [Step-by-Step Guide]

Data analytics is one of the key industries in the last few years. Everyone wants to be a part of this fast-growing industry, and a lot of students are enrolling in data analytics courses day by day. However, by just enrolling in a course, you won’t be able to grab a high-paying job in this field. 

Therefore, you must know how to start a career in data analytics before investing your hard-earned money in any courses or training, which will not be enough to get your desired role in data analytics. 

Steps to Build a Career in Data Analytics

The journey to becoming a highly paid data analytics professional is a challenging one. There are many aspiring professionals in the industry, each of them with a cursory knowledge of the field. So, it entirely depends on the caliber of the candidate and how soon they land a secured job in data analytics. 

However, before we begin with the steps, here are some prerequisites that you need to develop to have a profitable career:

  • Basics of programming languages
  • An arranged portfolio of your past works containing relevant projects. 
  • A compelling resume and an optimized LinkedIn profile. 
  • A decent network of data scientists engaged with data analytics projects. 

Step 1: Gain the Essential Skills 

Here is what you need to do to gain the necessary skills in data analytics. 

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Get Your Basics Right

To better understand data analytics, one needs to start with mathematics, programming, and statistics lessons. However, if you have not studied these subjects and are wondering how to start a career in data analytics, you don’t need to worry. 

Even a person without the knowledge of these subjects can excel in data analytics. The quality of observed and study data is not inherently good, but it is what one develops over time and with practice. It would be best if you built curiosity about mastering data analytics. 

Some key skills you need to master are the fundamentals of statistics, probability, and measure of spread and center, along with distribution and learning to test hypotheses. 

Work on spreadsheets and try to perform data analytics on them. 

This way, you will get a feel for collecting and wrangling data. After this, you can level up to Power BI, perform some exploratory work there, and work with other tools. 

Find the Right Programming Language

Data analytics around the globe work with different types of programming languages. So there are no right or wrong choices here. However, it would help if you had a grasp of SQL language to learn how to work with databases, execute queries, and manipulate databases for analytics. If you are looking for a good course on SQL, then you can start with CCSLA’s Introduction to SQL Databases course.

So, if you are asking how to start a career in data analytics, you should also be asking yourself what the right programming language is for you. You can choose from a wide array of languages, such as Python, R, etc. 

After choosing the language of your choice, learn how to clean, manipulate, and visualize data in the programming language that you have opted for. Have a look at various libraries that the programming language has to offer and find out if any of them can help you with data analytics tasks. 

Step 2: Get Qualified in Data Analysis Skills 

If you have an interest and skills to analyze data and work as a professional in this field, get some formal qualifications. Such qualifications will validate your skills, learning, and practical work experience in data analytics. Most employers look for credentials or any bachelor’s or master’s degree in data analytics while hiring potential candidates. 

Many universities and institutions offer online training courses for data analytics. You can get a data analytics degree in a bachelor’s or master’s program easily. This will provide you with in-depth knowledge and skills. 

Moreover, the cost is not huge but only ranges between $30,000 and $200,000. Apart from this, you can also opt for data science boot camps and hands-on experience. These boot camps offer flexibility and accessibility to the candidate. 

With more advanced training, you can efficiently learn and work together. To get a qualification in data analytics, you need to follow a particular path, which starts with a master’s in a programming language like Python or R. If you are interested in learning Python, you can enroll in the Python Foundations eLearning course. 

Qualifications Route

You can enhance your learning with some significant qualifications. Here are some essential qualification routes that you can opt for. For further reference, we have provided its pros and cons to help you know how to start a career in data analytics with better qualifications. 

University Degree

You can opt for any university degree that involves coding and machine language and is related to software or data analysis. 

Pros

  • This degree will offer you a certain curriculum that boosts your knowledge in this field. 
  • Networking opportunities will be there where you can build your network with people in this field. 
  • On completion of this degree, you will have recognized credentials. 

Cons 

  • This degree may be time-consuming as its duration is 2 to 4 years. 
  • It is expensive and costs around $30,000. 
  • It may involve unrelated courses. 

Online Bootcamps

Online boot camps help you get practical experience in data analytics and make you a pro in a shorter span. 

Pros

  • Online boot camps are focused on curricula related to data analytics.
  • The duration of the bootcamp is around 3 to 4 months. 
  • This is more affordable and does not involve much cost. 

Cons 

  • It offers an intense pace for completion. 
  • No networking opportunities are offered.

Certifications

If you are looking for a way to know how to start a career in data analytics, certifications are the most powerful way to boost your career. Many certifications are there that you can opt for. Some of them are CompTIA Data+ and Apache Spark for Data Scientists.

Pros

  • It offers a wide range of topics and validates your skills in data analytics. 
  • It is quick to complete and can be completed at your own pace. 
  • It is widely recognized, and almost every global company recognizes these certifications. 

Cons 

  • To get certification, sometimes you are required to have prior experience or knowledge. 
  • Certain costs can add up. 

Step 3: Practice Skills Required in Data Analytics

The skills you acquire must be practiced to gain a better grasp of concepts. Data analytics requires the best-skilled professionals. So, it is important to develop your skills and master them to become an expert. 

If you need to learn how to start a career in data analytics and practice skills, you can start by working on your individual projects and by using free dataset projects.

Working on Individual Projects 

You can get suggestions from your curriculum and start working on your own projects to gain abundant opportunities to learn and grow. Practicing makes a man perfect, and thus, you need to solve mocks of real-world-related concepts. 

This type of effort will give you a solid foundation for future work experience. You can use your data science projects to put your skills into the project and avoid the brainstorming process.

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As you gain experience in in-depth analysis, you will have to expand your scope and ability to adapt advanced studies. You should know how to sell yourself and create your own brand value. With work experience and research, you can build one. 

Working on individual projects will help you know how to fetch data and base your research. You will learn how to check and make hypotheses and communicate effectively. Though working on an individual process may take a long time, it will be worth your time and future. 

Make Use of Free Datasets for Projects 

You should work smartly when you are working on any project, whether it’s your own or a curriculum project. To explore relevant data, you must use free datasets that you can find in various online repositories. The free datacamp projects are also available that you can use to get an idea of the project and know how the structures are built. 

Some of them are as follows: 

DataCamp Workspace 

This is based on an online integrated development environment (IDE), which is available for free. These datasets are best for analyzing data, writing coding, and practicing your skills. Use this dataset to become fluent in your skills. 

UCI Machine Learning Repository 

This repository contains almost all the open-source datasets and is well-documented and structured. Use this repository to deal with your data analytic journey. 

Kaggle 

The Kaggle stores thousands of datasets that involve topics and relevant data values that you can use in real-time projects. This dataset will help you work on individual projects and familiarize you with advanced data analysis, too. 

This repository is based on the Google search engine and is keyword-centric. It’s just like the regular Google search, which stores over 25 million free datasets. 

FiveThirtyEight 

This platform stores data-driven articles on data analytics and offers interactive topics that you can search to learn more detailed concepts. 

Step 4: Create Your Portfolio of Projects

If you have prior experience in data analytics and have projects that you can show in your resume, you must create a collection of all those projects and make an attractive portfolio.

As an entry-level data analyst, you will mostly have capstone projects from your university degree, certification course, or online boot camps. It is okay to have these initial-level projects to add to your portfolio.

Source 

With time and experience, your work will be added to the portfolio. So, don’t hesitate to mention your projects and skills in your portfolio, as it will help recruiters determine your abilities. You should also have abilities like soft skills and technical skills to make an attractive appeal. The portfolio will prove your broad scope to the recruiter and highlight your qualities through soft skills and effective communication. 

Things to Include in Your Portfolio

Though there is no formula or structure for your portfolio, you can put certain essential things in your portfolio that can highlight you in front of the recruiters. 

Here are some elements that you can include:

  1. Data Cleaning Projects – Include them in your portfolio if you have worked on such projects. You can also prepare raw data for your analysis.
  1. Statistical Analysis – A project based on statistical analysis is good to include in your portfolio. Make predictions and insights based on your data analysis. 
  1. Exploratory Data Analysis (EDA) Projects – This type of project is explanatory about your analytical abilities. So, demonstrate these projects by summarizing and visualizing data analysis. 
  1. Business Analysis Projects – Try to include projects that exhibit your data-driven approach and derive insights based on the business metrics. 
  1. Soft Skills – Try to include a presentation or report that showcases your inner self and soft skills. 

Step 5: Fill Out the Job Applications

Follow certain factors before filling out your form. Following are some essential points that you should be aware of. 

Create Professional Resume

The resume is a very important aspect in almost every industry. So, prepare your resume and include every minute detail. Keep it professional and compelling to capture the interest and attention of the recruiters. 

To create your resume, first know how to start a career in data analytics and what skills you need to acquire. This way, you can mention specific skills and knowledge in your resume and make it more data analytics-centric. 

Here are some key elements that the best resume includes: 

  • Prepare your on-page resume. 
  • Choose a better resume template. You can make it from scratch or use popular templates such as Canva, ResumeCoach, Zety, CakeResume, etc. 
  • Create your master CV and include details with eye-catching words, use pointers, and avoid using sentences. 
  • You must customize your data analytics experience according to the job description and increase your chances of getting hired. 
  • Include below key information: 
  • Objective
  • Contact information
  • Work experience
  • Projects worked on
  • Skills and Education 

Get Your Skills Right

While applying for a data analyst job, you must ensure that you have revised all the fundamental skills you have learned so far. 

Some of the critical technical skills that you need to brush up on before the interview are: 

  • SQL
  • The command line
  • Statistics 
  • Python or R
  • Data analysis 
  • Web scraping 
  • Data storytelling 
  • Data cleaning and wrangling 
  • Dealing with unstructured data 

You also need to have a good command over soft skills such as:

  • Curiosity 
  • Creativity 
  • Flexibility 
  • Decision making 
  • Analytical thinking
  • Communication skills 
  • Team skills 

Network with Leading Data Analysts

Networking is one of the things that you need to do even before you start looking for tips on how to start a career in data analytics. You can start this by making a compelling profile on LinkedIn, GitHub, or any other networking platform. 

Furthermore, you can connect with analysts in community groups, conferences, or social media groups if you want to network. This peer group will keep you updated about the latest trends and will let you upskill yourself with the skills that are in demand in the job market. 

Reach Out to Employers with Openings

Apart from getting ready for your interviews from job applications, you can reach out to employers of companies directly in which you are interested in a job. This is a less conventional path, so you need to put extra effort into it and reach out to them using their official email IDs. 

All you need is to craft your mail, keeping in mind the core ideals and values of the company. You can also talk about improving the company’s products or offerings. This will make you an ideal candidate for the job as every company prefers a person who can solve their problems and has a working knowledge of the services and values offered by the company. 

Bottomline

All the aforementioned steps are tips on how to start your career in data analytics. So what are you waiting for? Realize your dream of becoming a successful data analyst by following these steps and securing that dream job of yours. 

If you wish to become a certified data analytics professional, you can opt for the best data analytics and engineering bootcamp that is offered by CCSLA. You will gain proficiency in languages including TSQL, Python, Tableau, Snowflake, TFS, and Azure DevOps in this course, which will help you expand your data analytics skill set. In addition, you’ll get the opportunity to network with professionals in the field and acquire trustworthy career guidance and mentoring. 

FAQs

Q1: What is Data Analytics?

Answer: Data Analytics involves examining raw data to draw conclusions, identify patterns, and extract actionable insights. It encompasses a range of techniques from various domains like statistics, mathematics, and computer science to analyze and interpret complex datasets.

Q2: What educational background is needed for a career in Data Analytics?

Answer: While many data analysts have degrees in fields like computer science, mathematics, statistics, or economics, it’s increasingly possible to enter the field with diverse educational backgrounds, especially if you acquire relevant skills through courses, bootcamps, or self-learning.

Q3: Are there essential skills I should learn to become a Data Analyst?

Answer: Essential skills for data analysts include proficiency in programming languages like Python or R, a strong understanding of statistical analysis, experience with SQL and databases, and the ability to use data visualization tools such as Tableau or Power BI.

Q4: How can I gain practical experience in Data Analytics?

Answer: You can gain practical experience by working on personal projects, participating in data analysis competitions, contributing to open-source projects, or pursuing internships and entry-level positions that involve data handling and analysis.

Q5: What are some common tools and technologies I should be familiar with as a Data Analyst?

Answer: Familiarize yourself with data manipulation and analysis libraries in Python (Pandas, NumPy) or R, database querying languages like SQL, data visualization tools (Tableau, Power BI), and basic knowledge of big data platforms (Hadoop, Spark).

Q6: What types of industries hire Data Analysts?

Answer: Data Analysts are in demand across various industries, including technology, finance, healthcare, retail, marketing, and government. Any industry that relies on data to make informed decisions may hire data analysts.

Q7: How do I build a portfolio to showcase my Data Analytics skills?

Answer: Build a portfolio by including projects that demonstrate your ability to collect, clean, analyze, and visualize data. Describe the problem, your approach, and the insights gained. Use platforms like GitHub or a personal blog to showcase your work.

Q8: What is the role of networking in starting a career in Data Analytics?

Answer: Networking can play a crucial role in starting your career. Attend industry meetups, conferences, and seminars. Participate in online forums and LinkedIn groups related to data analytics to connect with professionals in the field.

Q9: Are there entry-level jobs that can lead to a Data Analyst position?

Answer: Yes, entry-level jobs such as Data Technician, Junior Analyst, or roles in data entry and reporting can provide foundational skills and experience. These positions often serve as stepping stones to more advanced data analyst roles.

Q10: How important are communication skills for a Data Analyst?

Answer: Communication skills are extremely important for Data Analysts. They must be able to clearly convey their findings, explain the significance of their analysis, and make recommendations based on data to both technical and non-technical audiences.

Q11: What is the typical career progression for someone in Data Analytics?

Answer: Career progression in data analytics might include advancing to senior analyst roles, specializing in a specific domain (like marketing analytics or financial analytics), moving into data science or business intelligence positions, or taking on managerial roles overseeing analytics teams.

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