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How To Start A Data Analytics Career?

How To Start A Data Analytics Career?

Data analytics is a field with a lot of opportunities, for companies across all industries especially telecommunications, insurance, advertising, financial services and healthcare industries. As industries started building out their analytics departments.

The growth is expected to increase into the future as there are still some of industries lagging in big data analytics and Data Science adoption – including education, government, and manufacturing.

To become a Data Analyst, you must acquire key data analysis skills and an ability to take insights from large data sets.

Start your career as Data Analyst in Five Steps:

  1. Learn data analytics fundamentals
  2. Work on different projects to develop your practical data analytics skills
  3. Develop visualizations and practice presenting them
  4. Develop a Data Analyst portfolio to showcase your analytical skills
  5. Apply to a relevant data analyst job

1. Learn Data Analytics Fundamentals

The first step towards Data analytics is to learn the data analysis fundamentals and advanced data analysis tools like Microsoft Excel; programming languages SQL, Python and R; Spark and Hadoop is used for number-crunching; and Tableau, Matplotlib, or ggplot2 for creating beautiful visualizations that bring data to life.

Data analytics training courses can be a great way to learn these fundamental data skills, giving you a strong footing on all these platforms, and the ability to gain hands-on experience with:

  • Data management
  • Statistical programming
  • Programming languages
  • Data cleaning
  • Data visualization

2. Work on Live Projects to Develop Your Data Analytics Skills

If you want to become a Data Analyst, you’ll need to get hands-on experience in Data Analyst’s basic tools. Once you’ve done so, you can start applying your knowledge to projects. Successful Data Analysts will need to keep up to date with the latest and greatest tools and skills associated with data analysis, including:

  • Knowledge of spreadsheets and querying languages like XML and SQL
  • Programming languages Python, R and frameworks like Hadoop
  • Visualization tools like Tableau, PowerBI also Plotly, Bokeh, and Matplotlib

Data analysts also need to have experience with one or several leading data analysis platforms, including Google Analytics and Adobe Analytics.

Practice on projects that touch on all the different stages of data analysis. Research on different companies and market opportunities, establish the parameters of the data you need to collect, clean the data, and then model and analyse it using custom-built algorithms.

Finally, turn the insights you’ve drawn from your analysis into beautiful visualizations, or organise them into a dashboard that allows others to query and interact with your dataset in a user-friendly way.

Data Analytics Tools List

  • XML
  • SQL
  • Python
  • R
  • Hadoop
  • Tableau
  • PowerBI
  • Plotly
  • Bokeh
  • Matplotlib
  • Google Analytics
  • Adobe Analytics

Try working on a range of practice projects that help to grow – and demonstrate – your ability to work with different types of data, mining structured data, text and images, audio, or video to perform statistical analysis and make predictions.

Data Analysis Steps

  1. Research companies and market opportunities
  2. Establish and determine the parameters of data you need to collect
  3. Gather and clean data
  4. Model and analyze data using custom-built algorithms

3. Develop Visualizations and Practice

 To become a Data Analyst, you should have hands-on experience in using programs like Tableau, PowerBI, Bokeh, Plotly, or Infogram. Practice building your own visualization methods from scratch, finding the best way to visualize the data.

Microsoft Excel is a powerful tool to develop visualizations. Making calculations or graphs by correlating the information in their cells is done through excel. Excel remains incredible for more than 30 years and is unavoidable in the field of data science.

Creating visualizations is the initial process. Data Analysts use these visualizations to present their findings, and they require strong presentation skills also.

4. Develop a Portfolio to Showcase Your Data Analyst Work

One of the most important steps toward becoming a Data Analyst is to decide how you will demonstrate your data skills and knowledge. A professional portfolio describing your expertise is a must. To get started, you can put the code you’ve written up on GitHub to show what you can do and begin building your professional portfolio.

Tips for Building a Data Analyst Portfolio on your own

  • Demonstrate code you’ve written on Github or another similar portfolio platform
  • Join Data Analyst communities like Kaggle to grow your skills and find inspiration
  • Research, develop and complete a personal project, please note to carefully document and visualize its creation

When it comes to choosing your projects, pick something that you’re really interested in, ask a question about it, and try to answer that question with data.

A well-executed data project made by you can be a great way to demonstrate your data analysis abilities and impress potential hiring managers.

5. Apply to Relevant Data Analyst Jobs

There are a wide variety of Data Analyst job roles in the job market. All of the following roles are based on data analytics and can be entry-level or more senior data roles.

There are many other variations in data analysis job roles available out there—new applications for data analytics are being developed all the time, and even the jobs will continue to evolve as data analysis becomes more prevalent for companies. Following are some of the data analyst job roles

  • Data Analyst
  • Database Administrator
  • Data Scientist
  • Business Analyst
  • Business Intelligence Analyst
  • Transportation Logistics
  • Statistician
  • Systems Analyst
  • Healthcare Data Analyst
  • Operations Analyst
  • Data Engineer
  • Quantitative Analyst
  • Data Architect
  • IT Business Analyst

Get Your Data Analytics Training From IPSR

By completing the course from IPSR the aspirants can achieve the ability to 

Demonstrate ability to engage in various roles and responsibilities of a Machine learning engineer, Practice analytical skills in data preparation, analysis and visualization using Python, data preprocessing, machine learning with Python, Will be able to Prepare Webapps with machine learning prediction tasks.

 

 

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