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Work From Home Data Analytics Jobs – A Complete Guide

With technological advancement and the pandemic forcing us to explore the world of work-from-home jobs, more and more companies opt for remote operations and offer this facility of remote jobs to their employees. Work-from-home Data Analytics jobs are becoming increasingly popular. This also helps the companies to access widespread talent and helps the employees to find work from any geography. Data analytics has been omnipresent in recent years and is well-suited for remote work. This article will give us insights into the world of work-from-home data analytics jobs.

Work from home data analytics jobs

What is Data Analytics?

Data is any systematic record or collection of facts and figures to be used for an examination or analysis. Data arranged in an organized form is called information. Data can be structured or unstructured data, quantitative and qualitative. Analytics in the field of computer science uses maths, statistics, and machine learning to identify patterns in data that are meaningful.

Analytics involves scrutinizing data to determine, infer, and share new understandings and knowledge. Data Analytics is an examination of raw data to extract useful conclusions about the information to make competent, data-driven business decisions and improve operations. Data analytics is done with the help of specialized software.

Work-from-home data analytics jobs is a role that requires to have access to the tools and resources that are necessary to work from home, including but not limited to a computer, internet connection, and necessary software. Such jobs can be full-time, part-time, or contractual.

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Why Data Analytics?

Data analytics plays a vital role in understanding trends and patterns from the huge data that is collected. It helps optimize performance, estimate results, and reduce costs. Data analytics aids businesses in understanding their end customers. These observations help plan for the future by keeping track of customers’ behavior towards products or services.

It can also help at a very generic level to identify the reason for low sales, why and which products people buy, the amount spent on these products, how to increase sales, etc. It helps industries improve their performance. Implementation of data analytics into the business helps in finding efficient ways of reducing costs.

As Data Analytics is an in-demand skill set, it is a perfect career choice for people who wish to fulfill their aspirations in this fast-growing field.

 

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Data Analysis vs Data Analytics

It is sometimes misconstrued that data analysis and data analytics are the same. Data analysis is done to examine the data to understand what has happened in connection to the data in the past whereas to find what will happen, data analytics is done. Data analysis refers to specific actions whereas Data analytics makes use of data and tools to make business decisions.

You should know the most important Data Analyst Interview Questions

 

Types of Data Analytics

There are 5 types of Data Analytics:

TYPEEXPLANATION
Descriptive AnalyticsThe commonly used and simplest form of data analytics. It analyses data finds trends, and describes WHAT has happened based on the available data.
Diagnostic AnalyticsIt describes WHY something happened in a particular manner. It helps to figure out how it happened and what can be done to stop recurrence.
Predictive AnalyticsAn advanced form of analytics that answers ‘WHAT WILL HAPPEN NEXT?’ in a business. It predicts the future consequence of a situation depending on the combination of the data related to both market trends and older data about your business.
Prescriptive AnalyticsComplex types of analysis to decide HOW the actions and decisions should be taken based on outcomes that have been predicted after assessing the different scenarios.
Cognitive AnalyticsIt is the most advanced and smarter form of analytics combining technologies like artificial intelligence, machine learning algorithms, and deep learning models to process the information and deduce from existing data and patterns.

 

Check here the top-ranked data analytics courses to get a high-paying job:

 

Educational Qualification for Data Analytics Job

Anybody with an inclination and a very strong foundation in Mathematics and a good understanding of statistics, linear algebra, and calculus, can pursue Data Analytics after the 12th (H.S.C or equivalent) to get a bachelor’s degree (B.Tech) and also at a postgraduate level.

Bachelor’s degree or equivalent in Science or Computer Science with 50% aggregate marks from a recognized university is a requirement for MTech/MBA programs. Apart from the conventional approach, many certification or diploma courses are also available.

Data Analytics courses cover skills like Statistics, Data visualization, Machine Learning Algorithms, Ensemble Techniques, Text Mining/ Natural Language Processing, Forecasting Analytics, and Hypothesis Testing. Online certification programs in Data Analytics can be in collaboration with big organizations like IBM, TCS, etc.

 

MBA in Data Analytics

An MBA degree in Data Analytics broadens the prospects for job opportunities. MBA graduates also get significant salary growth. MBA in Data Analytics covers data analysis skills and business skills. Students learn about business intelligence, data analytics, and information technology.

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Technical and Other Skills Required for Data Analytics Job

Data Analytics job requires speed, agility, and performance to utilize their full potential. It is a specialized field that requires a combination of technical and analytical skills. Some of the skills required for work-from-home data analytics jobs include:

Technical skills: A strong base in statistics, data mining, and machine learning is essential for data analytics. Job seekers should have a good understanding or experience in working with databases, programming languages such as Python and R or SQL, and data visualization tools.

Analytical skills: Good understanding of business requirements and translating them into data models. Ability to collect, analyze, and interpret large amounts of data and identify patterns and trends in large data sets to help businesses make informed decisions.

Domain Knowledge: Some companies who have openings from mid-level prefer hiring candidates having expertise in a specific domain like Healthcare, Banking and Financial, Retail, Logistics and Supply Chain Management, Entertainment, etc.

Communication skills: To be able to efficiently communicate the technical findings to a person from a non-technical background in a clear and easy-to-understand manner either in a written or verbal format.

Problem-solving skills: Identifying problems related to data quality and integrity and developing creative solutions is a must-have skill to excel in the job.

Time Management Skills: Managing your time effectively, setting deadlines, prioritizing tasks, and managing the workload without supervision will take you places.

 

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Tools and Technology Required for  Work-from-home Data Analytics Jobs

Hardware and Software: A good computer or a laptop with good speed and storage is a primary requirement for work-from-home data analytics jobs. Basic software such as Excel, and Google spreadsheets, and Statistical Analysis software such as SAS or SPSS for conducting more complex work.

Technologies Required: R Tool, Python, Tableau, Apache Spark, MySQL, and Azure are some of the technologies that the candidate must have on the machine.

Internet Connection: Another vital requirement is a strong and reliable internet connection to ensure large file transfers.

Communication Tools: Remote employees must have communication tools such as email, instant messaging, and a webcam for video conferencing in place to stay connected with their team.

Collaboration Tools: To share files and work on projects together tools such as Google Drive and Dropbox are important.

Sample Summary of Job Description for a Data Analytics Profile

  • Responsible for business growth.
  • Building solutions using advanced predictive modeling and statistical analysis
  • Exposure to the application of analytics to real work problems around process optimization, prioritization, classification, segmentation, targeting, and forecasting.
  • Experience in building products with embedded analytics or cloud technologies.
  • Any experience in problem spaces such as customer analytics or campaign analytics is an added advantage.

The above parameters are to be met considering the below focus weightage

Guidance, Insights, and Growth Enablement (30%)

Organizational and Operational Leadership (40%)

Customer Success (30%)

 

Sample Interview Questions for a Data Analytics Profile 

Prepare yourself with a thorough knowledge of the organization you are going to be interviewed at. As Data Analytics involves problem-solving, the candidate should have made a brief study of all the problems that the organization is currently facing and also research the potential problems that the organization may face. The interview questions vary depending on the level you join, from freshers, mid-level, or senior-level.

Some questions that may help –

  • What are the 3 phases of Data Analytics? – Raw Data, Information, and Knowledge
  • What are the steps in data analytics? Define Goals. Gather Data. Data Wrangling. Analysis. Comprehend Results.
  • What are the 7 analytical methods? Regression. Monte Carlo simulation. Factor analysis. Cohort analysis. Cluster analysis. Time series analysis. Sentiment analysis.
  • What is the best quality for a Data Analytics job? Good with numbers. Great IT skills. Familiar with databases and query languages. Good analytical skills. Good at solutions. Attention to detail and accuracy.
  • Can you co-relate Data Analytics in day-to-day life? In day-to-day life to find some solution we think about what happened last time or what will happen by choosing that particular decision. This is like thinking of what happened in the past or finding a solution for the future to decide on that basis.

A Typical Work Day for a Data Analytics Job Profile Candidate 

  • Data is divided by a range of different criteria such as age, location, population, etc. The values of the data can be in numbers or any other category data.
  • Data can be collected through numerous sources which are organized for further scrutiny with the help of Excel spreadsheets or other types of software specifically designed for the purpose.
  • The data is then cleaned up to confirm that there is no duplication or mistake.
  • It is done with prevalent analysis techniques namely, cluster analysis, time-series analysis, etc.
  • Findings are presented in simple language in the form of a chart or graph.

Advantages of work-from-home Data Analytics Job

There are numerous benefits of remote work, and data analytics jobs are no different –

Flexibility: One of the biggest benefits of working from home in the data analytics field is the flexibility it offers in terms of timing as well as work location. Working remotely allows you to work from the comfort of your own home, a coffee shop, or a self-created or decided working space, at any time that is ideal as per convenience for those who have other responsibilities such as caring for children or elderly family members or education or when they are most productive.

Employees can manage the work according to their preferences and can work at their own pace. This helps in achieving a better work-life balance and reduces stress which is essential for employee well-being.

Save Costs: Remote work eliminates the need for a daily commute, which can save you both time and money. Money that is saved on fuel, parking, public transportation, or eating out is substantial. Work-from-home jobs in data analytics are cost-effective for employees and employers equally.

While employees save on transportation, and lunch expenses, employers, save on overhead costs incurred on rent of the office space, utility bills, and other costs associated with the maintenance of an office and still deliver high-quality work for clients. And since every penny counts this can add up to a lot of savings over time.

Increase in Productivity: Employees who work from home tend to be more productive than those who work in an office as you can work in an environment that is comfortable. The discretion to take breaks and manage your time most effectively leads to high-quality and faster result-oriented output.

Global Opportunities: Work-from-home jobs give opportunities to work with clients or companies across the globe, irrespective of your location. This enables you to have diversified work experience to add to your resume.

 

Access to a Wider Talent: Employers can hire employees from all over the world and no longer have to hire local talent, as they have access to a wider pool of highly qualified and experienced candidates.

Zero Commutation: Shuttling to work is time-consuming and tiring. The long hours spent in traffic or on public transport are non-productive. Working from home saves time, and energy and reduces stress.

Work-Life Balance: Working from home improves work-life balance. You get more time with your family and friends and enjoy a dedicated time for hobbies and interests, including exercise.

Disadvantages of work-from-home Data Analytics Jobs

There are some challenges we should be aware of –

Seclusion:

A human being is a social animal and hence likes to be in groups. Remote work can give a feeling of disconnection as there is no face-to-face social interaction with co-workers. This leads to difficulty in collaborating and getting feedback.

Distractions: There are many distractions in the home environment, like small children, pets, the noise of the mixer grinder or the cooker whistle, or elderly people at home watching TV at high volume which makes it difficult to stay focused.

Technical Issues: Working from home requires a strong, stable internet connection and consistent hardware and software. Technology sometimes gives away at the most crucial time, internet outages or software glitches impact productivity and performance. It is important to have a reliable backup plan ready.

Effective Communication: It is difficult to coordinate, share information, and collaborate when the team is working from different locations while trying to avoid miscommunication at the same time.

Monotonous: We may tend to work for long, irregular, and outside office hours and may find it difficult to maintain routine and manage time effectively, also it can be difficult to separate work life from personal life.

Unclear Boundaries: People constantly keep themselves involved in office work by checking emails, which can decrease job satisfaction.

Hiring Data Analytics Candidates for Work-from-home Data Analytics Jobs

Ensure you modify your resume as per the job description and have a cover letter highlighting the relevant skills and experience.

LinkedIn: It is an excellent platform for a job search in data analytics when you filter your search by selecting work-from-home data analytics jobs.

Glassdoor: It is a great job search engine to narrow your search to remote jobs with a piece of additional information like company reviews, work culture, and salary information.

 

Indeed: One of the popular job search websites for work-from-home data analytics jobs.

FlexJobs: As the name suggests this platform focuses on flexible job opportunities. The search for work-from-home data analytics jobs, part-time jobs, or hybrid-mode jobs in data analytics can show very good results.

Remote.co: It gives a list of remote job opportunities which can be very helpful to the candidate.

Apart from these, even the individual organization portals can have remote job opportunities where the candidates can directly apply. The demand for data analytics experts has been constantly on the rise. Also, it is predicted to grow further in the next 10 years.

You Can Choose Your Specialized Career Path Out of the Following Numerous Job Roles for Work-from-home Data Analytics Jobs:

 

Data Analyst:

Responsible for collecting and analyzing the data. They use statistical tools to identify trends and patterns, and they prepare reports to help businesses make informed decisions. Data analysts are responsible for collecting, processing, and performing statistical analyses on large datasets. They are also responsible for presenting their findings clearly and concisely to stakeholders.

Business Intelligence Analyst:

A business intelligence analyst is responsible for analyzing business data to identify trends and insights. They develop dashboards and reports to help stakeholders make data-driven decisions.

Business intelligence analysts are responsible for collecting and analyzing data to identify trends and provide insights that can help businesses make informed decisions. They work closely with stakeholders to understand their requirements and present findings clearly and concisely.

Data Scientist:

A data scientist is responsible for developing and implementing machine learning algorithms to analyze and interpret data. They also develop predictive models to help businesses make informed decisions. Data scientists are responsible for developing algorithms and predictive models to identify trends and patterns in data. They work with stakeholders to understand their requirements and present their findings clearly and concisely.

Data Engineer:

A data engineer is responsible for designing and maintaining data pipelines. They ensure that data is collected, stored, and processed securely and efficiently. Data engineers are responsible for designing, developing, and maintaining data architecture systems. They work closely with stakeholders to understand their requirements and ensure that the systems meet their needs.

 

Machine Learning Engineers:

They are responsible for developing and implementing machine learning models to help businesses make informed decisions. They work closely with stakeholders to understand their requirements and ensure that the models meet their needs.

Best Practices to Be Successful in Work-from-home Data Analytics Jobs

Dedicated Workspace: Uninterrupted work helps you to stay focused and productive during working hours. Make sure your workspace is comfortable, with a good amount of fresh light and air, and free of distractions. Also, keep water and things you may need handy so that you don’t have to get up and distract yourself.

Clear Communication: Ensure all communication channels like video conferencing, instant messaging, and email are in place to stay in touch and share information to avoid miscommunication.

Organized Schedule: Distinct bifurcation between your work life and personal life is essential. Establish and stick to a work schedule, and avoid an imbalance between the work schedule and personal responsibilities.

Acquire new skills: Updating your knowledge is essential to stay competitive in the data analytics field. Online courses or webinars are easily available to keep abreast with the latest technologies.

FAQs

What is the salary for Work From Home Data Analytics Jobs?

Salary for a fresher to mid-level ranges from 7 lacs to 15 lacs per annum.

Which all industries require Data Analytics?

Almost all industries or businesses in any domain have job opportunities in the field of Data Analytics.

Is data analytics a good career?

In simple words, YES as there is no risk or danger of jobs being extinct in this field.

Is data analytics a tough job?

The job is technically demanding, which can sometimes be challenging but can be overcome with dedicated efforts.

Which is the best geography to work in?

Remote working has brought the world closer, hence you can choose any opportunity anywhere.

Is Data Analytics considered an IT Job?

Yes. It is termed as an IT job.

Conclusion

To conclude work from home data analytics job is a satisfying career choice. It is a boon and a blessing for working women, especially who have embraced motherhood recently and don’t want to give up on their careers and efficiently manage both worlds. Also, with some prerequisites taken into consideration, any person (irrespective of his age, location, domain expertise, or experience level) can learn Data Analytics for career growth and to broaden the scope of opportunities in the work-from-home data analytics jobs market.

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