Today, virtually every company has become a data-driven organization, which means they are implementing a strategy to acquire more data about their customers, markets and business processes. This data is then classified, stored and analyzed to make sense of it and derive actionable insights.

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What is Data Analytics?

Most businesses are constantly collecting data, but that data is meaningless in its raw form. This is when data analytics comes into play. Data analytics is the process of analyzing raw data to extract meaningful, actionable insights that can then be used to inform and drive intelligent business decisions.

A data analyst will take raw data, organize it, and then analyze it, changing it from unintelligible statistics to coherent, understandable information. After interpreting the data, the data analyst will share their findings in the form of suggestions or recommendations for the company’s next steps.

Data analytics allows you to understand the past and predict future patterns and behaviors; instead of relying on guesswork, you can make informed decisions based on what the data shows you.

Armed with data insights, businesses and organizations can gain a much deeper understanding of their audience, industry and company as a whole – and as a result, are much better positioned to make decisions and plan ahead.

Check out our blog at Data Science Tutorial to learn more about Data Science.

Why is data analysis important?

Data analytics plays an important role in business improvement as it is used to gather hidden insights, develop reports, conduct market research and improve business requirements.

Data analysis is a type of internal arrangement where numbers and figures are presented to management. With the help of data analysis, businesses will be able to make decisions about customer trends and predict behavior, increase business revenue and promote effective decision making.

Role of Data Analytics:

  • Gather hidden insights – Data is mined for hidden insights, which are then analyzed in light of business requirements.
  • Generate reports- Reports are generated from the data and disseminated to relevant teams and individuals to address further steps for successful business.
  • Conduct market analysis- Market analysis can be used to determine competitors’ strengths and weaknesses.
  • Improve business requirements – Data analytics enables businesses to better meet the needs and expectations of their customers.

Certification in Bigdata Analytics

Data analysis tools

With the growing market demand for Data Analytics, various tools with different functionality have emerged. The best data analysis tools, whether open source or user-friendly, are as follows.

  • Python: It is the most widely used analytical tool for statistics and data modeling. R can be compiled and run on a variety of systems, including UNIX, Windows, and Mac OS.
  • R programming: Python is an object-oriented programming language that is open source and easy to learn, develop, and maintain. It includes machine learning and visualization libraries such as Scikit-learn, TensorFlow, Matplotlib, Pandas and Keras.
  • Table Public: It’s a free program that connects to any data source, including Excel and enterprise data warehouses. It then generates visualizations, maps, dashboards and other web-based tools with real-time changes.
  • SAS: This tool, which is a programming language and environment for data manipulation and analysis, is easy to use and can analyze data from various sources.
  • Microsoft Excel: It is a popular data analysis tool. This tool looks at tasks that aggregate data with pivot table previews and is mostly used for internal customer data.
  • Apache Spark: This tool runs applications on Hadoop clusters 100 times faster in memory and 10 times faster in storage, making it one of the largest engines for large-scale data processing. This tool is also widely used to develop data pipelines and machine learning models.
  • RapidMiner: Advanced integrated platform capable of integrating with any type of data source including Access, Excel, Microsoft SQL, Teradata, Oracle and Sybase. This technology is typically used for predictive analytics such as data mining, text analysis, and machine learning.

Prepare for the industry by going through these Data Analyst Interview Questions now!

How to become a data analyst

Academic qualifications

A strong CGPA and a graduate degree from a data analytics program is recommended. Even if one does not specialize in data analysis, a degree in mathematics, statistics, or economics from a reputable university can lead to an entry-level data analyst position.

A bachelor’s degree is required for entry-level data analyst positions. Higher-level data analyst jobs typically pay more and may require a master’s degree. Apart from the degree, a person interested in becoming a data analyst can enroll in online courses.

Skills

Programming languages: A data analyst should be familiar with at least one programming language. R, Python, C++, Java, MATLAB, PHP and other programming languages ​​are among those that can be used to edit data.

Data management and manipulation: The data analyst should be familiar with programming languages ​​such as R, HIVE, SQL and others. Developing queries to extract relevant data is a critical component of Data Analytics.

A data analyst is responsible for providing management with accurate and reliable information. Therefore, data analysts must have a deep understanding of the data as well as the specific requirements of each user. Good communication skills are also required when working with others to ensure that data is effectively aligned with objectives.

Mathematical Ability: A data analyst must understand statistics and be familiar with the formulas needed to analyze data to provide real-world value. As a data analyst, you need to understand math and be able to deal with common business problems. You also need to understand how to use tables, charts, graphs, and other tools.

Microsoft Excel: The primary responsibilities of data analysts include organizing data and crunching numbers. As a result, it is important that the data analyst is familiar with Excel.

Career scope in Data Analytics

A data analyst can expect to make a significant amount of money, do fascinating work, and have a very stable job. It is a career that is always changing and varied and requires a lot of attention to detail and a focus on quality. A career in data analysis also provides excellent prospects for advancement.

Data Analyst is a position that is clearly on the rise. The difference between mid-level and senior-level positions is determined by experience and additional education. However, because data analysts are in such high demand at all levels, job growth is expected to be positive for each level over the next decade, ranging from 5% for a financial analyst to 25% for an operations research analyst.

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