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What is Data Analytics: Types, Tools, Career and Applications

  • April 04, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Data analytics has become an essential and important tool for organizations across all industries in recent years.

With the exponential growth of digital data and the emergence of sophisticated data analytics tools and techniques, companies are now able to collect, process, and analyze vast amounts of data to derive valuable insights that can inform the business decisions and strategies.

Data analytics enables businesses to make informed decisions based on empirical evidence, rather than relying on intuition or guesswork.

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By analyzing data, companies can identify patterns, trends, and relationships that may not be immediately apparent, and use this information to optimize their operations, improve customer experience, reduce costs, and increase revenue.

Data analytics is particularly important in today's fast-paced, data-driven business environment, where companies must be able to respond more quickly to changing market conditions and customer preferences.

By harnessing the power of the data analytics, businesses can gain a competitive edge by identifying and capitalizing on opportunities that others may miss.

The Basics Of Data Analytics

What is Data Analytics?

Data analytics is termed as the practice of using data, statistical algorithms, and computer software to extract the insights and knowledge from structured and unstructured data.

In other words, data analytics involves examining large datasets to uncover the hidden patterns, correlations, and other useful information that can help organizations make informed decisions.

The data used in analytics can come from a variety of sources, including customer transactions, social media posts, sensor data, and more. Once the data is getting collected, it must be cleaned, organized, and processed before it can be analyzed.

This involves using specialized software tools and techniques to identify and remove errors and inconsistencies in the data, as well as to transform the data into a format that can be very easily analyzed.

Once the data is prepared, data analysts can use a range of statistical and machine learning algorithms to analyze the data and also identify patterns and insights. This can include everything from simple statistical techniques like mean and variance, to more complex techniques like clustering and neural networks.

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Types of Data Analytics:

Data analytics is defined as a broad field that encompasses a range of techniques and methodologies for examining data and extracting insights.

One way to classify different types of data analytics is by the purpose they serve. Here are four main types of data analytics:

1. Descriptive Analytics: Descriptive Analytics involves summarizing and analyzing historical data to understand what has happened in the past.

2. Diagnostic Analytics: This type of analytics involves examining data to understand why something happened in the past.

3. Predictive Analytics: This type of analytics involves using statistical and machine learning models to predict future outcomes based on historical data.

4. Prescriptive Analytics: This type of analytics involves using advanced mathematical models and optimization techniques to recommend actions based on predicted outcomes.

Understanding the different types of data analytics can help organizations determine which techniques and methodologies are best suited to their specific needs and goals.

Data Analytics Tools and Technologies:

Data analytics involves processing and analyzing large amounts of data to uncover insights and patterns that can help organizations make informed decisions. To accomplish this task, data analysts use a variety of tools and technologies to collect, process, and analyze data.

Statistical tools are used to analyze data using advanced statistical models and techniques and are often used in scientific research and academia. Programming languages like Python and SQL are widely used in data analytics for their ability to process and manipulate large datasets, as well as for their flexibility and extensibility.

Spreadsheets (Excel, Google Sheets):

Spreadsheets are computer programs that allow users to organize, manipulate, and analyze data in a tabular format. Two of the most widely used spreadsheet programs are Microsoft Excel and Google Sheets.

Both of these programs have similar functionality and can be used for the wide range of data analysis tasks, including:

1. Data Entry and Management: Spreadsheets are often used to store and manage the large amounts of data. Users can easily enter data into cells and organize it into rows and columns, and can also sort and filter data to quickly find specific information.

2. Calculation and Analysis: Spreadsheets allow users to perform calculations on data, such as adding up numbers or calculating averages. They also provide a range of built-in functions and formulas for more complex calculations, such as statistical analysis and financial modeling.

3. Visualization and Reporting: Spreadsheets can be used to create charts, graphs, and other visualizations to help users understand their data. They also support the creation of reports and dashboards that can be shared with others.

One advantage of using spreadsheet programs like Excel and Google Sheets is their accessibility and ease of use. These programs are widely used and are available on most computers and mobile devices, making it easy for users to access their data from anywhere.

However, spreadsheets do have some limitations, particularly when it comes to managing large datasets or performing complex analyses. In these cases, more advanced data analytics tools and technologies, such as statistical software or programming languages, may be more appropriate.

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The Basics Of Data Analytics

Business Intelligence Tools (Tableau, Power BI):

Business intelligence i.e, the (BI) tools are software applications that are designed to collect, process, and analyze the data to help organizations make an informed business decisions. Two of the most widely used BI tools are Tableau and Power BI.

Tableau is a data visualization tool which allows the users to create interactive dashboards and reports. It supports a wide range of data sources, including spreadsheets, databases, and cloud-based services, and provides a range of data modelling and manipulation features.

Tableau's drag-and-drop interface makes it easy for users to create custom visualizations and explore their data in real time.

Power BI is another popular data visualization and reporting tool that is part of the Microsoft Office suite. It integrates seamlessly with other Microsoft applications, such as Excel and SharePoint, and can be used to create interactive dashboards, reports, and data visualizations.

Power BI also supports a wide range of data sources, including spreadsheets, databases, and cloud-based services.

Both Tableau and Power BI have a range of features and capabilities that make them powerful tools for data analysis and visualization. These include:

1. Data Visualization: Both tools allow users to create custom charts, graphs, and other visualizations to explore and analyze their data.

2. Data Modeling: Both tools provide a range of data modeling and manipulation features, such as pivoting and filtering, to help users transform and prepare their data for analysis.

3. Integration:Both tools support a wide range of data sources and can be integrated with other data analytics tools and technologies.

4. Collaboration:Both tools allow users to share their reports and visualizations with others, making it easy to collaborate and communicate insights with colleagues and stakeholders.

Overall, business intelligence tools like Tableau and Power BI are powerful tools for data analysis and visualization and are widely used by organizations of all sizes and industries. By using these tools, businesses can gain valuable insights into their data and make informed decisions that can drive growth and success.

Steps in Data Analytics:

Data analytics involves a systematic process of collecting, processing, analyzing, and interpreting data to uncover insights and patterns that can inform decision-making. The process typically and generally involves the following steps:

1. Data Collection: First step in the data analytics is to collect data from various sources, including databases, spreadsheets, and external sources. This data may be structured or unstructured, and may come in various formats, such as CSV, Excel, or JSON.

2. Data Cleaning and Preprocessing:Once the data has been getting collected, it needs to be cleaned and preprocessed to ensure its quality and usability. This involves removing duplicates, correcting errors, and transforming data into a usable format.

3. Data Analysis: The next step in this is to analyze the data using a range of statistical and analytical techniques to identify patterns, trends, and relationships. This may involve using software tools like Excel, R, or Python to run analyses and generate visualizations.

4. Data Interpretation and Insights: Once the data has been analyzed, the next step is to interpret the results and derive insights from the data. This may involve identifying correlations, making predictions, or drawing conclusions based on the data.

5. Reporting and Communication: The final step is to communicate the results and insights to stakeholders in a clear and concise manner. This may involve creating reports, dashboards, or presentations that effectively convey the findings to decision-makers.

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Data analytics is a complex and iterative process that involves multiple steps and requires a range of skills and tools. By following a structured approach and using the right tools and techniques, organizations can gain valuable insights from their data that can inform business decisions and strategies.

Applications of Data Analytics:

Data analytics has a wide range of applications across industries and sectors, and is increasingly being used to inform decision-making and drive business growth. Some of the key applications of data analytics include:

1. Business Intelligence: Data analytics is commonly used to provide business intelligence insights that can help organizations make informed decisions. This may involve analyzing sales data, customer behavior, or market trends to identify opportunities for growth and optimization.

2. Healthcare:Data analytics is being increasingly used in the healthcare to improve the patient outcomes and reduce costs. This may involve analyzing patient data to identify patterns and risk factors, or using predictive analytics to identify the patients who are at risk of developing certain conditions.

3. Finance: Data analytics is widely used in finance to identify the trends and patterns in financial data, monitor risk, and support decision-making. This may involve analyzing market data, financial statements, or customer transactions to identify opportunities for growth and reduce risk.

4. Marketing:Data analytics is commonly used in marketing to analyze customer behavior and preferences, identify market trends, and optimize marketing campaigns. This may involve using data to segment customers, personalize messaging, or track campaign performance.

5. Manufacturing:Data analytics is being increasingly used in manufacturing to optimize processes, improve quality, and reduce waste. This may involve using data to identify bottlenecks, optimize production schedules, or predict equipment failures.

Data analytics is a powerful and crucial tool that can be applied in the wide range of contexts to improve decision-making and drive business growth. As organizations increasingly getting rely on data to inform their strategies and operations, the applications of data analytics are likely to continue to expand and evolve in the years to come.

Career in Data Analytics:

• Data analytics is a rapidly growing field that offers a wide range of the career opportunities for individuals with strong analytical and problem-solving skills. As businesses and organizations increasingly rely on data to inform decision-making and drive growth, the demand for the skilled data analysts is on the rise.

• A career in data analytics typically involves working with large datasets to extract insights and identify patterns that can inform business decisions. This may involve using statistical software and programming languages like R and Python to perform data analysis, creating visualizations and reports to communicate insights, and collaborating with stakeholders to understand business needs and objectives.

• There are a variety of both roles and specializations within the field of data analytics, including data scientist, data analyst, business intelligence analyst, and data engineer. Individuals with a background in statistics, mathematics, computer science, or a related field are well-suited for careers in data analytics.

• The job outlook for data analysts is promising, with a projected job growth rate of 25% over the next decade. Salaries for data analysts are also competitive, with median annual earnings of around $83,000 per year.

• A career in data analytics offers a challenging and rewarding path for individuals who enjoy working with data and using analytics to drive business outcomes. With strong demand and promising job prospects, it's an excellent field to consider for those interested in pursuing a career in analytics.

The Basics Of Data Analytics

Final Thoughts and Recommendations:

1. Keep Learning: Data analytics is a rapidly evolving field, so it's important to stay up-to-date with the latest tools, technologies, and best practices. Consider taking online courses or attending industry conferences to continue learning and developing your skills.

2. Develop Soft Skills: In addition to the technical skills, it's important to develop soft skills like communication, problem-solving, and teamwork. These skills can help you collaborate effectively with colleagues and stakeholders and ensure that your data analysis efforts are aligned with business objectives.

3. Practice Ethical Data Use: As a data analyst, you'll have access to sensitive data, so it's important to practice ethical data use and protect the privacy and security of the individuals and organizations.

4. Network:Building a professional network can be valuable in securing job opportunities and staying up-to-date with industry trends. Consider joining industry groups or attending networking events to connect with other data analysts and professionals in the field.

5. Experiment and Explore: Data analytics is a field that rewards creativity and innovation, so don't be afraid to experiment and explore new approaches to data analysis. By staying curious and open-minded, you may be able to uncover insights and opportunities that others have overlooked.

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Conclusion

In conclusion, data analytics is a crucial field in today's data-driven world. It allows businesses and organizations to derive insights and make data-driven decisions that can lead to improved performance and growth. From analyzing customer behavior to predicting market trends, data analytics has a wide range of applications across industries.

In this tutorial, we have covered the basics of data analytics, including its importance, types, tools and technologies, steps involved, applications, and career opportunities. We also provided some final thoughts and recommendations to help individuals interested in pursuing a career in data analytics.

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