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As far as technology is concerned, a lot has changed in the past decade. Among these are YouTube, Google, Facebook, Instagram, E-Mail, etc. Since then these platforms were mere hobbies or leisure-time experiences. Yet, with the upcoming and growing influence of these platforms, so much can be accomplished from an educational point of view, informational, promotional, business deals, etc.
Indeed, with the ever-increasing demand for these platforms, there is a need to research the framework, algorithm, and other related topics. Furthermore, pursuing such schooling or a career in this industry is referred to as Data Analyst. To illustrate, data is like history; it describes the prior experience, performance, and also the process’s direction.
In the case of a large or small company, ignoring data, statistics, and data volumes could cost potential growth direction, as it plays a critical role in making business decisions. Since then, they’ve been leveraging data to uncover company weaknesses that need to be addressed. Emails, text messages, and YouTube videos watched are all part of the almost 2.5 quintillion bytes of data generated every day throughout the world. Similarly, by utilizing big data, Netflix saves $1 billion each year on client retention. Isn’t that incredible?
Similarly, a data analyst does exactly the same thing. However, they interpret statistical data and transform it into useful information that companies and organizations can use to make important decisions.
Eventually, this is where the world is today. Since all businesses, from small MSMEs to multinational corporations, rely on data to better understand their markets and customers and generate actionable insights. As a result, data analysis has become one of the most in-demand careers in the world, with data analysts in great demand from the world’s largest companies.
What is a Data Analyst?
A data analyst reviews and interprets computer, and statistical data and transforms raw data into information business insights. Since they play such an important role in predicting market trends and analyzing future market scenarios.
For instance, if you enjoy problem-solving, have a passion for mathematics and information, and dream of using your expertise to generate money for your company, become an information analyst.
Similarly, as a data analyst, you would collect data and extract useful information from it to assist firms in optimizing their plans. In addition, you’ll also be analyzing data related to consumers, goods, and the company’s performance in order to aid in real-time decision-making. Meanwhile, you’ll utilize data analysis tools as you go deeper into the process of evaluating data.
So, who should a company target in its next advertising campaign? Which item is suitable for a particular age group? And what actions are related to fiscal spending?
Since these are the kind of questions you may need to answer as a data analyst. Therefore, read on to learn more about what a data analyst is, the skills you need, and how to start your journey to become a data analyst.
Data Analyst Duties: Roles and Responsibilities
A data analysts collect, process, and analyze large datasets to provide actionable business insights. They use their knowledge to answer questions and solve problems.
Since then, rapid advances in technology and the development of languages such as SQL have made it easier to retrieve data from databases. Therefore, knowledge of MS Excel and SQL is two things that every data analyst should have as a weapon.
As a result, besides analyzing the data, you also remove corruption, evaluate the data quality, determine its meaning, and generate a report based on that analysis for your company’s management.
On a daily basis, many data analysts do the following:
- Gather data: Analysts often collect the data themselves. Similarly, this may include conducting surveys, tracking visitor characteristics on company websites, or purchasing records from data collection specialists.
- Clean data: Raw data can contain duplicates, errors, or outliers. However, cleaning up the data means maintaining the quality of the data through a spreadsheet or programming language so that it is not misinterpreted or biased.
- Model data: The structure of the database is created and designed. Because of this, you can choose what data types you want to store and collect, how to link the categories of data, and how to display the data.
- Interpret data: Interpreting data is finding patterns and trends in the data that can help you answer the question at hand.
- Presentation: Communicating the results of your insights will be an important part of your work. Since, you compile visualizations such as charts and graphs, create reports, and present information to stakeholders.
Examples of job roles you can take as you become a data analyst
- Sales Analysts
As a sales analyst, you’ll be responsible for assessing the company’s sales activity on a regular basis. Therefore, this includes a survey of sales volume and the overall sales pipeline.
- Marketing Analysts
Your main focus is on identifying crucial advantages and weaknesses in your organization’s various promotion platforms.
- Product Analysts
Similarly, their job includes determining specific items and parts of the company’s products, as well as making product price proposals.
- Business Analysts
Likewise, they assist businesses to improve processes and systems by doing research and analysis to address business problems.
- Budget Analysts
In addition, a budget analyst examines the organization’s budget and seeks new ways to boost profits and efficiency. They are in charge of allocating funding to various departments.
What tools do data analysts use?
Analysts use a variety of technologies to make their work more accurate and efficient during the data analysis process. Since then, some of the most widely used data analytics tools include:
- Microsoft Excel
- Google Sheets
- R or Python
- Microsoft Power BI
- Jupyter Notebooks
Types of data analysts
- Descriptive analytics look back at what happened in the past, such as monthly revenue, quarterly sales, and annual website visitors. Because these types of discoveries enable an organization to recognize trends since they are useful.
- By comparing informative data sets to uncover connections and patterns, diagnostic analytics considers why something happened. Because this aids an organization in determining the cause of a positive or negative result.
- By spotting tendencies in descriptive and diagnostic data, predictive analytics aims to predict expected outcomes. Also, it enables a business to take proactive measures, such as reaching out to a customer who is unlikely to renew a contract.
- Prescriptive analytics aims to determine the best course of action for a company. Because this type of analysis adds so much to the ability to solve potential issues or beat the competition of industry trends. Since it frequently necessitates the use of complex algorithms and modern technology like machine learning.
The firm PwC determined that descriptive analytics is insufficient for informed, data-driven decision-making in a 2016 poll of more than 2,000 company executives. As a result, organizations are increasingly relying on tests and data analytics.
Data analysts may have a variety of job titles, including:
- Medical and health care analyst
- Market research analyst
- Business analyst
- Business intelligence analyst
- Operations research analyst
- Intelligence analyst
Data Analyst Skills Required
- Software systems and databases are designed and maintained, which includes addressing coding errors and other data-related issues.
- Data mining from primary and secondary sources, then restructuring the information into a manner that can be read by humans and technology.
- Analyzing datasets using a statistical approach that focuses on trends and patterns that may be useful for diagnostic and predictive analytics.
- Demonstrating the importance of their job in the areas of local, national, and international trends that affect their company and industry.
- Creating executive reports that use applicable data to effectively express trends, patterns, and recommendations.
- In cooperation with programmers, engineers, and organizational executives, identifying opportunities for process improvements, advocating system upgrades, and implementing data governance policies.
- Creating appropriate documentation that allows stakeholders to understand the data analysis process and, if needed, reproduce or repeat the analysis
Data Analyst Qualifications and Training requirements
It is not sufficient to have a deep understanding and knowledge of data analysis software or technologies. Furthermore, solid quantitative, critical thinking, and analytical skills are required to become a skilled data analyst. As a result, a bachelor’s degree in mathematics, computer science, economics, or statistics will offer you a major competitive advantage when it comes to starting a career as a data analyst. However, in the midst of a pandemic, data science, and business analytics PG programs give you a huge boost and help you adapt to the industry.
Data Analyst technical skills:
- Programming Languages (R/SAS): Data analysts should be fluent in one language and have a basic knowledge of several other languages. Similarly, data analysts use programming languages such as R and SAS for data collection, data cleaning, statistical analysis, and data visualization.
- Creative and Analytical Thinking: Curiosity and creativity are important qualities of a good data analyst. Therefore, while it is important to have a solid knowledge of statistical methods, it is even more important to think about the problem of creative and analytical lenses. In addition, this helps analysts generate interesting research questions that improve the company’s understanding of the problem at hand.
- Data Visualization: Trial and error is required for effective data analysis. For this reason, successful data analysts understand the type of graph to use, how to scale the visualization, and the graph to use depending on the audience.
- SQL Databases: In SQL, structured data is stored in a relational database. Similarly, data is stored in tables, and data analysts retrieve information from various tables to perform analysis.
- Database Querying Languages: The most common query language data used by analysts is SQL, and there are many variations of this language, including PostgreSQL, TSQL, and PL / SQL (Procedural Language / SQL).
- Data Mining, Cleaning, and Munging: If the data is not properly stored in the database, data analysts will need to use other tools to collect unstructured data. But once you have enough data, you can clean it up and process it through programming.
- Advanced Microsoft Excel: Data analysts need to be good at using Excel and have an understanding of advanced modeling and analysis techniques.
- Machine Learning: Data analysts with machine learning skills are very valuable, but machine learning is not expected in the job of a regular data analyst.
What tools do data analysts use?
Here are a few more useful tools that data analysts utilize on the job:
- Google Analytics (GA): Analysts can use GA to understand customer data, including landing pages and customer experience trends, and areas that need improvement.
- Tableau: Analysis and aggregation of data are done using Tableau. Because you can create dashboards and share them with different team members to create visualizations.
- Jupyter Notebook system: Data analysts can test their code with ease thanks to Jupyter notebooks. This is because non-experts prefer the simple design of Jupyter notebooks for markdown functionality.
- Github: The Github platform allows you to share and create technical projects. Therefore, it is a must for data analysts who use object-oriented programming.
- AWS S3: AWSS3 is a cloud storage system. Therefore, data analysts can use it to store and retrieve large datasets.
Paths to becoming a data analyst
Mastering these skills is the first step in becoming a data analyst. Also, here are some ways you can go to get them flexible enough to fit your life.
- Professional certificate: Professional certification programs for beginners usually do not require prior knowledge in this area. It teaches you basic skills such as SQL and statistics while giving you the opportunity to create portfolio projects and provide real-time feedback on your work. Similarly, some of Henry Harvin’s professional qualification programs do just that.
- Bachelor’s degree: The Bureau of Labor Statistics recommends a bachelor’s degree in work that involves data analysis. Since, if you are considering a degree in data analysis, you can gain an edge over your potential employer by focusing your coursework on statistics, math, or computer science. As a result, many online bachelor’s degree programs have flexible schedules that allow you to customize your degree according to your priorities.
- Self-study: It is possible to obtain the skills required for data analysis on your own if you prefer a path that does not require formal training.
Data Analyst Salary: How Much Does a Data Analyst Make?
Does a data analyst job description excite you enough? If not, take a look at the salary for this freelance role. However, keep in mind that the salary of a data analyst depends on many factors such as education level, location, related experience, and skills.
As a result, the average yearly compensation of a seasoned data analyst might range from $60,000 to $140,000. Similarly, financial and technology companies tend to pay more than the average.
Since then, the average cross-market data analyst compensation has been around $73,528.
However, data analysts usually advance to higher-level positions such as senior data analysts, data scientists, data analysis managers, and business analysts. Similarly, the higher the responsibility, the greater the salary. Therefore, the average annual salary for data scientists is estimated to be around $ 95,000, and the average annual salary for analysis managers is estimated to be around $ 106,000.
Similarly, in the analytics industry, data analysts have different duties. In addition, here are some of the jobs of top data analysts on salaries in India and the United States:
|Job Role||Average Salary in USA p.a. in USD||Average Salary in India p.a. in INR|
|Data Analyst||$41,600-$93,600||Rs 3,00,000 – Rs 12,00,000|
|Analytics Manager||$78,000-$1,50,000||Rs 9,00,000 – Rs 31,00,000|
|Business Analyst||$50,000-$97,200||Rs 3,00,000 – Rs 15,00,000|
|Data Engineer||$65,000-$1,40,000||Rs 4,00,000 – Rs 19,00,000|
|Data Architect||$80,200-$1,70,000||Rs 11,00,000 – Rs 34,00,000|
|$49,000-$1,20,000||Rs 3,00,000 – Rs 15,00,000|
|Research Scientist||$60,000-$1,60,000||Rs 4,00,000 – Rs 28,00,000|
|Research Analyst||$41,600-$85,000||Rs 2,00,000 – Rs 9,00,000|
|Statistician||$55,000-$1,28,000||Rs 3,00,000 – Rs 19,00,000|
Since these are approximate ranges, so salaries will vary depending on years of experience, organization, skills, location, and many other factors. Meanwhile, the above data is provided by LinkedInSalaryChecker in the United States and Glassdoor in India.
Top Companies Hiring Data Analysts
If you are one of the candidates looking for a job as a data analyst, you can choose from over 86,000 vacancies around the world. It’s shocking, isn’t it? Since there are many different industries out there, but unlike all of them will benefit from data analysis. Nevertheless, today’s data analyst job positions are divided into different fields such as finance, healthcare, business, pharmacies, and marketing.
Currently, in the United States and Europe, business intelligence firms have the most job openings for data analysts, followed by banking, sharing economy services, healthcare, and entertainment businesses.
Some of the world’s leading companies hiring data analysts include IBM, TCS, Wipro, Infosys, Amazon, Flipkart, Intel, Netflix, Google, Intuit, Facebook, Apple, and CISCO Systems. Small businesses include Focus KPIs, Affinity Solutions, and Norgate Technology. Similarly, financial giants like Paypal and Barclays without a doubt hire data analysts in various sectors.
Data analyst career advancement
Being a data analyst can also open the door to other careers. Many data analysts will continue to work as data scientists because they enjoy the challenge and the opportunity to use their analytical skills to make a difference. Like analysts, data scientists use statistics, mathematics, and computer science to analyze data. However, scientists can use advanced techniques to create models and other tools to gain insights into future trends. Explore other career paths open to data analysts, such as management, consulting, or specializing.
Henry Harvin’s Big Data analyst course
This course provides an overview of Hadoop and Spark frameworks and how they can be used to process big data workloads. Similarly, it also covers the tools and technologies that are used in big data ecosystems, such as HDFS, YARN, MapReduce, and Hive.
9 in 1 course
- 40 hours of live online lessons in an interactive classroom.
- Soft skills, research, drafting, technical writing, academic writing, and other assignments are available.
- Internship: Try to put what you have learned into practice.
- Placement: 100% placement guarantee for 1 year after successful completion
- Access tools and techniques for e-learning, video content, exams, and more
- Boot Camp: Regular boot camp will cover the next 12 months
- Membership: 1-year acquisition
- Hackathon: Free access to hackathons and contests
Gold Membership Benefits
- Avail 1Year Gold Membership of Henry Harvin®️ Analytics Academy that includes E-Learning Access through recorded Videos, Games, Projects, CPDSPe Studies
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- Internships with Henry Harvin® or partner firms are guaranteed.
- Weekly 10+ job opportunities offered
- Experience Industry Projects during the training
- Gain knowledge of the Hadoop framework
- Works with HDFS and Hadoop 2. xYARN
- Get comprehensive knowledge about Mapreduce
- Learn the basics of Hive illustrations and loading various file formats
- Manipulate external tables in Hive and load data into Hive tables
- Handling query operations on Hive tables
- Learn about Apache Kafka as a distributed streaming platform
- Configure Spark to manage performance optimization and data serialization
- Qualify as a background for demanding big data analysts
- Bridging the gap between thousands of undeveloped, high-paying jobs in the big data analytics industry with a shortage of talent
- As a business person, make efficient and correct decisions for big data frameworks
- As a leading certified big data analyst, it will be a valuable asset to your organization
- Explain all the features of the big data analytics framework in a complete business case
- The course guarantees that your profile is distinguished among your colleagues, while you are pursuing a career in the field of the analyst of data with gratifying skills and experiences.
- Rewarding Certification Get Big Data Analyst Certification
- Enhance your resume and LinkedIn profile with professional development
Big Data Analytics Course Curriculum
1. Hadoop: Master big data
2. Hive: Big Data SQL
3. Spark: Big data streaming and analysis
4. Apache Kafka distributed streaming platform
5. Advanced Spark
6. Supplementary Module 1: Soft Skill Development
- Corporate communication
- Preparing for a job interview
- Presentation ability
7. Complimentary Module 2: Create a resume
Analysis connects theory and practice to identify and communicate data-driven insights, enabling managers, stakeholders, and other leaders within an organization to make more informed decisions.
Similarly, experienced data analysts take into account various external factors within the organization, such as when considering their work. In this case, this includes things like the competitive environment, internal and external business interests, and even the lack of specific data sets in the database. As a result, they rely on the suggestions offered to stakeholders to help them in their decisions.
Therefore, the Master of Analysis program provides students with the skills and knowledge which seem necessary for a career as a data analyst. Similarly, the program covers concepts such as probabilistic reasoning, statistical modeling, data visualization, and also risk management in a corporate environment. In addition, the program provides students with exposure to programming languages, database languages, and software programs, since they are essential to the day-to-day work of data analysts.
Experience with databases and data analysis tools is required. You should be able to write SQL queries and procedures, understand Microsoft Excel, Matlab, and IBM SPSS, analyze trends, and gain accurate insights.
I think one of the greatest strengths that can be applied to a data analyst position is the strength of the problem solver.
Define your goals.
Decide how to measure your goals.
Analyze the data.
Visualize and interpret the results.
Data analysts act as gatekeepers for your organization’s data so that stakeholders can understand the data and use it to make strategic business decisions. This is a technical role that requires a bachelor’s or master’s degree in analysis, computer modeling, science, or mathematics.
Data Science Course
The Data Science Course from Henry Harvin equips students and Data Analysts with the most essential skills needed to apply data science in any number of real-world contexts. It blends theory, computation, and application in a most easy-to-understand and practical way.
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