Complete Guide to start your Data Analytics Career

If you desire a career option in data analytic, this article serves as a complete guide for you to comprehend data analytics briefly. It provides a brief review of the future scope of data analysis, data analytics jobs & various data analytic jobs for freshers.

These technologies are utilized in business by Data Analyst to alter relationships, also, to settle on profitable business decisions. Besides, it masters to break down logical models, speculations, and theories.

What is Data Analytics?

Major points regarding Data Analytics

• Firstly, the career in data analysis mainly alludes to an arrangement of utilizations, from fundamental business intelligence (BI), information revealing, and online analytical preparation (OLAP) to various sorts of advanced analytics.
• Secondly, Data Analyst helps businesses to increase revenues, improve operational potency, to promote campaigns and client service efforts. It responds to rising market trends and enhances a competitive edge over others.
• Thirdly and most importantly, The data dissected can contain individual authentic records or new information. In addition, this is taken care of by periodic analysis and updated from a mix of internal frameworks and external information sources.
• Moreover, there is a requirement of some vital elements for any initiative in Data Analysis. By combining them, a successful DA initiative can provide a clear image of where you are, wherever you have been, and where you ought to go. Furthermore, the article covers various career options in data analytics, data analytics jobs, multiple choices of data analytics jobs for freshers, current scenario, and future scope of data analysis.

Key Components of Data Analytics

Data analytics career focuses on the key components mentioned below:

Descriptive analytics

This is often the primary method of depicting historical trends of data. It aims to answer the question, what happened? However, it typically involves measuring traditional indicators like return on investment (ROI). The symbols used are entirely different for every industry. It does not make predictions or directly informs selections. In addition, it focuses on summarizing data in an exceedingly purposeful and graphic means.

Advanced Analytics

This vital part takes advantage of advanced tools to extract data, build predictions, and discover trends. In addition, these tools embrace classical statistics and machine learning. Machine learning technologies like neural networks, natural language processing, sentiment analysis, and additional change in advanced analytics are used actively in advanced analytics. However, this information provides new insight from data. Advanced analytics addresses what if? Queries.

Machine Learning and Big Data Analytics

The opportunity of machine learning techniques, massive data sets, and low-cost computing power has enabled the employment of DA in several industries. However, the collection of large data sets is instrumental in allowing these techniques. In addition, it permits businesses to draw substantive conclusions from complicated and varied data sources, that has been made attainable by advances in parallel processing and low-cost computational power.

Types of Data Analysis

Each one comprises of  contrasting  considerable  goals and a specific position in the data analysis process:

Descriptive analytics

This procedure orders enormous datasets to disclose results to stakeholders. By developing key performance indicators (KPIs), it will facilitate track successes or failures. Several Enterprises improve parameters to trace the achievements with the help of this method. The collection of relevant data, the data analysis process, and data visualization, providing essential observation into past performance is required. Specific organizations use return on investment  (ROI) as a critical metric.

Diagnostic analytics

Answer questions about why things happened. These techniques supplement additional features of basic descriptive analytics. The process analyzes findings from descriptive analytics and digs deeper to seek out the cause. The performance indicators are used to discover why they got better or worse. The process usually happens in 3 steps listed below:

• Identify anomalies in the data, which may be unexpected changes in a metric or a specific market.
• Data that are associated with these anomalies is collected.
• Statistical techniques are wont to find relationships and trends that specify the unexpected changes, ‘anomalies’.

Predictive analytics

Helps predict what will happen in the future. These techniques use historical data to spot trends and confirm if they are likely to recur. These tools offer valuable insight into what might happen in the future, and its techniques embrace a range of statistical and machine learning techniques, like neural networks, call trees, and regression.

Prescriptive analytics

• Answers the query regarding what is being done? By using insights from predictive analytics, creates data-driven decisions. It enables businesses to create informed choices in the face of uncertainty. Prescriptive analytics techniques have faith in machine learning strategies that can find patterns in large datasets. Through the analysis of past decisions and events, the likelihood of various outcomes can be predictable.
• These varieties of data analytics offer the insight that businesses compel to create effective and efficient choices. Used in combination, they provide a comprehensive understanding of a company’s desires and opportunities.

Steps involved in Data Analytics

The primary steps in the data analytics method are data mining, data management, statistical analysis, and data presentation. The importance and balance of these steps rely upon the data used and the goal of the report. Basically, in simpler terms, the steps are- Define, Measure, Analyze, Improve, Control (DMAIC) the most effective analytical tool.

Data Mining

It is a vital process for several data analytics tasks. It involves extracting data from unstructured data sources. These may embrace written text, complicated, complex databases, or raw sensor data. The essential steps are to extract, transform, and in addition to load data (often referred to as ETL.) However, these steps to convert raw data into an accessible and manageable format that prepares data for storage and analysis. It is usually the most time-demanding task in the data analysis pipeline.

Data Warehousing

Data management in other words data warehousing is a crucial facet of this domain. It involves designing and implementing databases that allow easy accessibility to the outcome of data mining. Also, it consists of creating and managing SQL databases. The frequently used databases are Non-relational and No-SQL databases.

Statistical Analyzing

This is the primary ingredient of data analytics. They are data-creation insights. Uses massive data to reveal trends in data. This applies to new data to make predictions and inform a higher cognitive process. Statistical programming languages like R or Python (with pandas) are essential to this process. Open-source libraries and packages like TensorFlow modify advanced analysis. ML and statistical analyses are dynamically utilized in this area.

Information Presentation

The final step in most information analytics processes is information presentation. It is often an important step that permits insights to share with stakeholders. Information mental image is commonly the foremost vital tool in information presentation. Compelling visualizations are essential to inform the story within the information, which can facilitate executives and managers to perceive the importance of those insights.

Applications of Data Analytics

The applications of data analytics are broad to choose Data analytics as a career. Analyzing big data will optimize efficiency in many alternative industries. Rising performance allows businesses to mark their existence in a progressively competing world—data analytics delivering great success in several different fields.

Finance

• One of the earliest adopters of DA is the financial sector. Data analytics features an immense role in banking as well, used to predict market trends and risk assessment.
• The primary data analytical practices carried out in the financial sector are risk analysis, real-time analysis, consumer analytics, customer data management, providing personalized services, fraud detection, and also algorithmic trading.
• Credit scores are a working example of data analytics that affects everybody. These scores use several data points to determine lending risk. So use is to notice and forestall fraud to boost potency and scale back risk for financial institutions. At financial institutions like investment banks, the management track is the commonest career path analysts take from the entry-level.
• However, on proving yourself in management, you have a bright career option in data analytics in this domain as a department head or vice president. There is an excellent future scope of data analysis in this sector. There is a unique opportunity for data analytics jobs for freshers in the finance sector

Insurance companies

It has excellent existence at large insurance corporations, credit bureaus, technology companies, and in almost any industry. Huge tech corporations like Facebook and Google analyze big data to a dizzying degree. To do so, they employ several of the top data analysts for a variety of functions, including advertising and internal analysis, in conjunction with an excellent deal of user analysis. It is a brilliant career option in data analytics with excellent future scope of data analysis in this sector. It is a unique opportunity for data analytics jobs for freshers in the insurance sector.

Business Insights

Many companies additionally label data analysts as information scientists. It involves working with a company’s proprietary database. Several information scientists work with core database infrastructures, thus also gaining skills in alternative applicable technical areas like data infrastructure building and development. A brilliant career option in data analytics with enormous future scope of data analysis is predicted in this sector. It is a unique opportunity for data analytics jobs for freshers in businesses.

Government Sector

The government sector is one such sector that employs and depends heavily on information scientists for data collection, mining, and analysis. The government sector is a good career option in data analytics and has a good future scope of data analysis with a wide array of data analytics jobs for freshers.

Scientific Research

Statistics and data analysis have always made their ways in scientific research, advanced analytic techniques and big data provide several new insights. However, these techniques can realize trends in advanced systems. Researchers are presently using machine learning to safeguard wildlife.

Healthcare and Pharmaceutical Sector

• Predicting patient outcomes, expeditiously allocating funding, and rising diagnostic techniques are simply a couple of examples of how data analytics is impacting the healthcare sector. However, machine learning revolutionizes the pharmaceutical industry, significantly improving data discovery.
• Drug discovery is an advanced task with several variables. Pharmaceutical corporations also use data analytics to grasp the marketplace for drugs, collect data & predict its actual sale value.
• The use of data analytics goes on the far side maximizing profits and ROI, however. Data analytics provides critical information for healthcare (health informatics), in addition, crime prevention, and environmental protection.

Internet of things Domain

• It is a field that is exploding alongside machine learning. The devices give an excellent chance for data analytics. IoT devices usually contain several sensors that collect substantive data points for their operation. Tools such as the Nest thermostat track movement and temperature to control heating and cooling. Practical tools like this can use data to learn from and predict your behavior. It will give advanced home automation that may adapt to the approach you wish.
• The applications of data analytics are endless. A lot of information is collected every day — this presents new opportunities to apply data analytics to additional parts of the business, science, and daily life subsequently.

Data Analytics Qualifications and Career Opportunities

Educational Qualifications of Data Analyst

• Apart from certification or diploma courses, Data Analytics is also available at the postgraduate level (MBA/ PGDM courses )in Data analytics and under graduation level( B.Tech/ B.E s a stream of specialization in Computer Science and Management.
• The minimum eligibility criteria for a PG Data Analytics course is a UG with at least 50% aggregate or equivalent, preferably in Science or Computer Science from a recognized university.
• You are graduating from a data analysis program, significantly with a good grade point average or a high ranking in your class, ought to result in an entry-level data analysis position without abundant bother.

Technical Skills and Experience

• One needs to have a thorough understanding of computer software(s) such as Querying Language (SQL, Hive, Pig), scripting Language (Python, Matlab), Statistical Language (R, SAS, SPSS), and Excel.
• Data analytics professionals should have excellent interpretive and problem-solving skills to explain the process of data analysis and its outcome.
• The top jobs and careers in Data Analytics can mark up to $100,000 annually during the initial phase of college if the performance meets the expectation. Experienced professionals can earn relatively more than an entry-level knowledge analyst makes.
• The experience will come from working as an entry-level analyst or from a related field, like investment analysis. However, education is usually the most vital factor in your resume when applying for a data analytics jobs.

Career Opportunities in Data Analytics

The list of some of the many different roles and career options in Data Analytics and goals that you may encounter when searching profession in Data Analysis include

• Business Analyst:- The data analyst job is to analyze business-specific data.
• Management reporting:-The individual reports data analytics to management on business functions.
• Corporate strategy analyst:-This role focuses on analyzing company-wide data and advising management on strategic direction and mergers in addition to acquisitions as well.
• Compensation and benefits analyst:-The expert is an active member of the human resources department that analyzes employee compensation and also benefits data.
• Budget analyst:-Aims at and works for the specified- budget analysis and also reporting.
• Insurance underwriting analyst:-The fellow analyzes individual, company, and also the industry data for decisions on insurance plans.
• Actuary:-The data analysts, in this case, analyze liability planning for insurance companies, data accumulated of mortality, accident, sickness, disability, retirement rates to create probability tables, and risk forecasting.
• Sales analyst:-Works on sales data that helps to support, improve, and also optimize the sales process.
• Web Analytics:-The individual analyzes a dashboard of analytics around a specific page, topic focus, or website comprehensively.
• Fraud analytics:-Individual monitors and analyzes fraud data subsequently.
• Credit analytics:-The credit market offers an absolute requirement for analytics and information science in lending risk, lending approvals. And also lending analysis credit reporting and credit monitoring.

Other Major Roles

• Business product analyst:-He/she focuses on attributes and characteristics analysis and also responsible for advice for management on the optimal pricing of a product/service based on market factors.
• Social media data analyst:-The growing tech companies, for instance, Facebook, depend on correctly used to build, monitor, and in addition, make advances to the technology offerings that particular platform relies upon, the job of the analysts to perform data analysis of the same efficiently. This can prove to be a good data analytics job for freshers
• Machine learning analyst:-Machine learning is a rapidly developing technology that includes programming and feeding machines to make cognitive decisions. However, ML analysts may work on different aspects, including data preparation, data feeds, analysis of results, and more.
• In addition, other profiles include Data Architect, Data Engineer, Statistician, and Database administrator.

• Companies hiring Data Analytics Professionals–
  • Companies such as Google, Fractal, Fractal Analytics, RBS, Yahoo, Tredence, Deloitte, Barclays, Expedia, Microsoft, Bridgei2i, Make my trip, Brillio, etc.
  • Financial Sectors, for instance, Kotak Mahindra, ICICI Bank, Citi Bank, HDFC Bank, Yes Bank, Axis Bank, Bank of America, etc.
  • In addition, social media platforms such as Facebook, Linkedin, Twitter, Instagram, etc.
  • Healthcare consulting firms such as ZS Associates, IQUVIA, WNS, etc.
  • Famous E-commerce shopping platforms such as Amazon, Flipkart Snapdeal, Myntra, etc.

Current Scenario of Data Analytics in India

According to the latest edition of the Indian analytics study, AIM research, in association with Analytix Labs, provides insights on all analytics domains and in addition, the state of analytics across different sectors, firms, and enterprises.

The report, currently in its sixth year, analyzes the direction in which the analytics markets head

Sector-Wise

• The analytics capability is no longer restricted only to the MNC and Domestic IT corporations and captive units of Banking corporations. However, the broader data Science domain has remodeled beyond just supporting business functions. Meanwhile, analytics has currently emerged as a necessary capability across organizations, with organizations developing data Science capabilities that transcend the complete business model and operation value chain of the firms. Analytics is no longer the aim of marketing, IT, and data-rich firms.
• Corporations across sectors and types, including Industrial and FMCG companies, are progressively adopting analytics and making investments of both capital and operational resources in the Data Science domain to realize a competitive edge up the market.
• To sum up, analytics is enabling various predictive and prescriptive capabilities across different organizations – these include recommendation and predictive modeling driven employing artificial intelligence (AI) and Machine Learning (ML).

Overview

• As per the associated reports concerning the development of the data science domain in India, it’s noticeable that the broad data science domain and precisely the analytics operation has experienced vital growth over the last year itself. However, the expansion in salaries on basis of across almost all parameters, on the basis of maturing of the analytics market in terms of experienced hiring and wages offered, the importance of gender diversity in the Indian analytics industry.

Facts regarding Data Analytics & AI

• The $762 Million investment in Indian Artificial Intelligence & analytics startups is self-explanatory because of the development and global standing of the Indian Analytics market.
• This development has expedited the rise in overall revenues of Indian analytics function to $ 35.9 Billion. It signifies a growth graph of 19.5% in year or year however in last year, the analytics operation garnered $30 Billion in terms of revenue.
• The function of analytics experienced growth across almost all companies, industry varieties, services rendered, in addition, geographies.

Key Highlights of Data Analytics in 2019-2020

Note:- The content research is done from the Source- “Analytics India Magazine” based on sector-wise distribution, city-wise distribution, in addition to the education qualification, experience level; and market projections.

• Up to March 2020, the analytics function in India earned consolidated revenues of $35.9 Billion – a 19.5% growth curve in revenue over 2019 subsequently.
• Revenue graphs depict reports for data across industries, company varieties, in addition to services, and segments.

Sector-Wise key highlights

• TCS continuously leads revenues across all enterprises (IT and otherwise) for both MNC and Domestic varieties, with $2.5 Billion in analytics revenue, up to 25th from $2.0 Billion in 2019.
• The Banking, Insurance or BFSI sector, and financial services added about 10.7% of the whole revenue share – the total revenues embrace the revenues of IT firms. However, excluding the taxes of IT companies, the companies across the BFSI sector contributed 35.6% of revenues – the maximum revenue contribution.
• Bengaluru leads the path for the largest share of revenues (city-wise) at 29.4% subsequently.
• 16% of the analytics revenues across all enterprises are attributed to advanced analytics, predictive modeling, and data science, subsequently up from 11 November in 2018 – this highlights the growing maturity and progression of the Indian data Science domain.

Future Scope of Data Analytics in India

Analytics services cowl descriptive, predictive, and prescriptive analytics, and in addition embrace data reporting, business intelligence, visualization, and analysis.

• The analytics domain accounts for 19% of the total revenues of the Indian IT and ITES market. This proportion had fallen from the 21st share last year. This fall is because of the Digital services phase experiencing very substantial revenue growth of roughly 25th y-o-y across the IT / ITES sector – the share of revenues from the digital domain has considerably accumulated across IT enterprises.
• The percentage of analytics about increase and rise to regarding 30 minutes of the IT industry by 2025. However, the revenues of the Indian IT & ITES industry would considerably be in the hands of Digital and Analytics services.
• Careers in Data Analytics is very wide and bright in the future as well.

Global Geography-Wise Revenue Distribution for Data Analytics

Graph summary of Global Revenue Distribution of Data Analytics

This revenue share by geography was researched by alternative secondary research sources, including whitepapers and case studies. With relevance to geography shows services to the USA garnered 56.6% of the revenue share, up from 47% in 2019. However, the revenues from UK targeted services garnered a 9.7% share – almost a dead ringer for 2019 was 9.6%. In addition, revenues from Australia come in the third position with 7.1% of the revenue share.

Company size and Employee Distribution

Graph summary – Number of Employees by company size

• The employees-distribution by the percentage or number of companies shows a concentration of personnel across two major categories –
• Niche category (1-200 employees) and
• The large-scale enterprise category (10000+ employees).
• 30% employees work for firms with niche categories, significantly rising from 29% in 2019 with the most significant number of professionals in the 10000+ employee category, which depicts the rise from 38% to 40%
• However, a significant percentage of experts prefer joining companies offering “stand-alone analytics- as- a –service.”
• Indian startups featuring unicorn status – privately held startups, which value more than $1 Billion while working with their clients practice the above method.

Demographics of Analytics Professionals in India

Graph Summary of Distribution by experience level

• The median work experience of analytics experts in India has declined significantly from 8 years in 2019 to 7.5 years in 2020. It was an outcome of 25,500 freshers added to the industry in 2019-20, as against 22,000 freshers added to the analytics personnel in 2019. Moreover, almost 41% of the professional analysts in India have work experience of fewer than five years, up from 37% in 2019.
• It signifies the great interest of the analytics domain to the IT workforce, who are choosing data analytics jobs over other IT professions.
• Preferably, experienced experts with more than ten years of experience constitute 23.2% of the analytics workforce from different sector-specific areas, such as FMCG, Telecom companies, and significant Industrials.
• The experience data mentioned here signifies a younger workforce. Moreover, the analytical roles of more significant impact offer to Indian professionals – the median earnings of analytics professionals are Rs. 14.4 Lakhs.

City- Wise Median Experience (in years)

Graph summary of City-Wise Median Experience

• Moreover, the city-wise median expertise data reveals Mumbai has the very best level of median employee experience– this is often common for the sector-location niche.
• However, a majority of Domestic Banks, Consulting Firms, MNC Banks, and Investment firms have their operational analysis based in Mumbai as these operations serve as a natural extension of the core financial and consulting businesses.
• Besides, distinctive domestic conglomerates, such as Reliance, Godrej, and the Tata Group, have head offices based in Mumbai as well. 
• Therefore, the average experience of analytics employees of Mumbai to the uppermost level is at 8.1 years, while the median exposure of Bengaluru is placed 2nd at 7.5 years.
• Quite a few niche Analytics companies, startups, and MNC in addition to Domestic IT companies have their operations centers located in IT Capital Bengaluru.

In short, personnel with considerable experience managing the analytics functions for global and local clients –  highlighting the average maturity of Bengaluru to the 2nd spot.

Market Projections- Future Scope of Data Analytics

Graph Summary of Indian Analytics Projections

• The Analytics industry predicts to grow at a CAGR of 16% till 2025 – the Indian analytics market in 2025 would reach $75 Billion. The Indian IT & ITES market, differently, is expected to grow at 6.1% CAGR – reaching $255 Billion in 2025. While analytics presently account for 19 (dropping from 21st last year) of the entire IT industry, the share of the analytics market is anticipates updated to extend. It will account for 30 minutes of the Indian IT sector till 2025.
• Digital services are experiencing tremendous growth among the IT sector – moreover digital currently accounts for approximately 29% of the revenue of the IT & ITES industry and has experienced 25% y-o-y growth in revenues.
• However the increase in the digital sector is predicted to moderate about 19% CAGR, therefore it enables the contribution of the analytics field to rise in terms of salaries & investments.
• Data of the graphs represented above are analyzed from the source-Analytix labs report 2020 in Analytics India Magazine.

Education levels of Data Analytics Professionals in India

• The pool of professionals from the diversity of educational backgrounds has undoubtedly grown over the last five years. However, the highest proportion of personnel is Non-MBA postgraduates at 27.3%.
• The total percentage of professionals from top-tier institutes is 16.2%. These institutes include the nation’s top universities, for example, IITs, IIMs, and other high-ranking Engineering and MBA institutes, such as BITs and NIT, and FMS, XLRI, XIM, NMIMS, and Spain respectively. The proportion of PhDs in Data Analytics is 1.6%. The scope of Data analytics career is very bright in the coming years.

Conclusion

• The content explains significance, application, employments, including data analytics jobs for experience and various data analytics jobs for freshers, current scenario, and the total revenue generation from different sectors by Data analytics. Moreover, the content also features multiple career options in the data analysis domain. Besides, it predicts the future scope of Data Analysis based on research and reports from various sources.
• Data analysts take extensive data and probe it to identify trends; they also build forecasts and extract data to assist their employers in creating effective business decisions. Above all, the career path you consider as a data analyst largely depends on your employer especially for data analytics jobs for freshers

Summary of trends related to Data Analytics

• The data analysts work in the healthcare industry, marketing, retail, financial institutions, and also almost all sectors.
• The revenue for the analytics domain elevated to $35.9 Billon – it illustrates the strength of the field “Indian analytics” as a whole, and in addition to the power of the talent pool subsequently.
• However, the demand regarding analytics services in India and the scope in Data Analytics career on a consolidated basis continued to grow through 2019-20.
• The career options in data analytics are multiple,data analytics jobs for freshers ,and the future scope of data analysis in India is massive and amazing.

In terms of Organizations

• Most of the large IT organizations are currently providing end-to-end Analytics-as-a-Service among the broader data Sciences domain.
• Large and medium-sized IT & consulting organization, TCS, HCL Tech, Accenture, and ZS Associates, are now scarcely providing stand-alone analytics services to their clients. 
• AI/ML strengthens the analytical function leveraged to automate aspects of data science and also ML models for development and deployment.
• While outsourcing is the primary driver of revenues, reported consolidates for both domestic and outsourcing services.
• Many internal IT and domestic-consumption drove enterprises to illustrate that there is a symbolic change.
• The applicant seeks the transformation from simple descriptive data analytics to high-end predictive and also prescriptive data analytics services.
• However, on the other hand, apart from IT and Consulting sectors, the BFSI sector leads the revenue segment, with a total of 35.6% of the revenue share.
• The Pharma sector, including captive and domestic enterprises, has committed 8.5% of the revenue share, above all continuing the trend of revenue jumps in the previous years and proves to be a good career option in data analytics and astonishing future scope of data analysis in this domain.
• Above all, Bengaluru emerges as a destination with the most significant revenue percentage – at 29.4%, indicating the location- appeal in terms of analytics talent and ecosystem.

Concluding Note

• The growth in Digital and Online segments across the Indian IT industry and other sectors, for instance, Media, e-commerce, FMCG, and Telecom, is featured by Data analytics.
• In conclusion, it predicts to grow at 16 PF CAGR till 2025, accounting for 30 minutes of the revenues of the IT / ITES industry – impacting employment and innovation in business methods through analytics-driven AI models and Prescriptive Analytics.
• Above all, it is a very bright career option in data analytics today, the advanced tech tomorrow, and in the future scope of data analysis is remarkable too.

FAQs for Data Analysis Career

Editor’s Note | Data Analytics Career

The effect of COVID 19 on the career and domain of Data Analytics is seen in the revenue generation during the 2020 COVID phase. Most of the crucial data analysis can be done from home but certain tactics require manpower and teamwork too which could not be possible during this pandemic time. You can track the LIVE status of COVID-19 updates on COVID-19 Pandemic Update Live. However, the applications and data analytics jobs have been maximized in this time phase due to an increase in online businesses and trends. There are various career options in data analytics as data analytics jobs for freshers and a wide range of future scope in the data analysis domain.