Career Path for Data Analytics–

Education, Experience, Jobs, and Pay

Data Analytics Training: 

A profession in data analytics requires extensive training in analytical techniques and data analysis models. Technical professionals can begin their careers as entry-level analysts and advance to the position of manager, then senior manager, and ultimately the C-suite.

A sizable portion of the economic hub of the twenty-first century is now data analysis. It involves gaining access to, purifying, altering, and processing data to make it useful to utilize it to make crucial decisions and important inferences. Every kind of institution and organization now receives a daily flood of data. These businesses employ the data analytics course as a vital component of daily operations, from vast industrial farms to municipal clinics. Data analysis is the process of transforming data into a form to inform future choices and boost productivity.

1. Professional Google Data Analytics Certification:

This 6-month data analysis training program is Google-certified, and after the program, students will receive a shareable, legitimate, and well-regarded credential. You can learn how to check for data integrity, use spreadsheets to discover data cleaning methods, create and apply Structured Query Language (SQL) queries for databases to clean and transform data, comprehend how to verify results, and visualize data findings and explore reports—all without any prior training in data analysis. 

2. Learnbay Data Analytics Course

Learnbay offers industry-accredited best data analytics course for working professionals looking to upgrade their skills. With its online interactive classes and project sessions, you can master the latest data analytics tools used by top companies. Its 6-month program will make you an expert and IBM-certified data analyst. 

3. Associate Microsoft Certified Power B.I. Data Analyst:

Microsoft offers an online certification program called Power B.I. Data Analyst Associate. By passing the test and receiving the certification, you can demonstrate that you are a skilled data analyst who can deploy and maintain assets and properly prepare, model, visualize, and analyze data using Power B.I. and Microsoft Azure. Before applying for the exam, you should have some prior experience and a basic understanding of data repositories and procedures.

4. Harvard Graduate Certificate in Data Analytics:

The Harvard Extension School offers this graduate certificate program in data analytics to both beginning and seasoned data analysts. Although the program can be finished in as little as 18 months, either online or offline, it can last up to three years. The graduate certificate program offers business analytics programming abilities, data visualization, and application to diverse areas in real life. It also teaches how to use statistical analyses and data mining approaches in real situations.

Skills to learn in Data Analytics training 

An individual cannot become a data analyst in a weekend or even a few weeks. It is a very professional position that calls for a specific skill set. However, a person with the right amount of motivation can successfully pick up the necessary skills from scratch. The detailed process for becoming a certified data analyst is outlined in the following steps.

1. Programming language: 

Every data analyst needs to have a basic understanding of programming languages. Python, SQL, and R are three popular computer languages that are helpful in data analytics. There are many others as well. By creating programs to clean, visualize, and analyze data effectively, these programming languages assist you in performing advanced analysis. Additionally, they aid the analyst in comprehending all other facets of data analysis.

The Python programming language does the cleaning, manipulating, analyzing, and displaying of data easier. R is essential for statistical analysis and visualization, and SQL is required to communicate with database management systems which can be mastered with the best data analytics course online.

2. Understanding statistics and math:

A data analyst needs to be proficient in statistics and the age-old subject of mathematics. To begin with, the data analyst constantly works with numbers and needs a solid understanding of a few mathematical and statistical concepts to succeed. A solid understanding of statistics can aid you in selecting the best algorithm, calculating the likelihood of events, analyzing the implications of the data obtained, seeing trends and patterns, and producing accurate findings. Even though it would be tempting to go right into data analysis, mastering statistics if your background is weak will set you apart from other data analysts.

3. Learning about data wrangling:

The term "data wrangling" refers to the processing of data. It is the procedure for enhancing accessibility by cleaning data, removing errors, and integrating data sets. Every data analyst needs to be extremely skilled in data wrangling. Raw, unprocessed data is transformed after data wrangling into a form that can be used with formulas and algorithms to get accurate results. Over 70% of a data analyst's time is spent on data wrangling.

4. Knowing the fundamentals of data visualization:

Your duties as a data analyst go beyond extracting insights from gathered data. Being able to present data in a simple way is an essential part of a data analyst's job. The findings you have come at as an analyst must be understood by stakeholders and other organization members. You may effectively present your work by learning data visualization techniques. Charts, graphs, maps, and other methods for graphically representing data are used in data visualization to make certain research or study easier to understand.

5. Developing a portfolio for data analysts:

Working diligently on projects is an excellent strategy. Projects refer to work for close friends and relatives who could be prepared to give you a try in exchange for a small favor. It also includes assignments, programs, quizzes, and competition entries from data analysis groups and online courses. Any cloud storage platform can be used to compile all of them into a neatly structured folder that you can share with potential workers. You can test out several websites like GitHub, Topcoder, and Kaggle.

Jobs and Pay Scales in Data Analytics:

1. Data Analyst

Any organization's data analyst is responsible for gathering, scrubbing, organizing, and processing data. The data analyst gains insights from the data that will aid in the organization's decision-making regarding particular matters like pricing, marketing, etc. A degree in a related discipline or completion of a certification program is required for data analysts. According to Payscale statistics, which was last updated on July 2, 2022, the average salary for a data analyst in the U.S. is approximately $63,486, excluding bonuses and commissions.

2. Business intelligence Analyst:

The crucial role of a business intelligence analyst was to help the organization advance by offering insightful analysis of the data. The business intelligence analyst examines all internal and external market information to discover trends and patterns that the company might benefit from. A bachelor's degree is a minimum requirement for this position, and advanced degrees may be requested. According to Payscale data that was most recently updated on July 2, 2022, a business intelligence analyst in the U.S. makes an average salary of around $71,395 annually.

3. Data analyst:

Developing the framework for data analysis is the responsibility of a data scientist inside an organization. It was part of testing data, creating data products, and executing algorithms. The data analyst who controls the show from the front end is in a less technical position than the data scientist. On the other hand, the data scientist makes sure that business decisions are made as accurately as feasible. A data scientist makes $99,398 annually, plus additional bonuses, according to Payscale data that was most recently updated on July 2, 2022.

4. Data Engineer:

A company's data architect is responsible for creating the structure required for an analysis to be effective. They collaborate with other team members to create the enterprise data management (EDM) system's blueprint and make it simple for employees to access corporate data. According to Payscale, the average take-home pay for a data architect is $123,486.  

Key Takeaway!

The Bureau of Labor Statistics predicts a 25% increase in demand for data analytics experts between 2020 and 2030. This is significantly higher than the norm across industries, indicating a positive job market for applicants with analytics training. Fortunately, Learnbay’s data science course with placement is available to get you started, and employment openings in almost all large corporations. These specialists may someday command six-figure salaries and join the MNCs.