what is data analytics in data science

What is Data Analytics in Data Science?

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One of the most common questions that occur in the field of data science is – What is Data Analytics in Data Science? 

Data analytics is the process of analyzing data to understand it and make decisions. It can be used to improve business processes, understand customer behavior and trends, and make decisions about which marketing campaigns to use. Data analytics is an important tool for any business that wants to improve how it processes data and use it to make better business decisions. However, many organizations struggle with implementing data analytics solutions because they don’t fully understand what it is or the benefits it can provide. In this post, I’ll explain what data analytics is and how it can be used to help your organization make better business decisions.

Why is Data Analytics Important?

Data comes in many forms, including transaction data, social media content, website visits, and other sources. Organizations collect this data from many different sources and use it for different purposes. For example, companies may collect information from customers when they sign up for a service or make a purchase. This information is used to provide them with services and support related to their purchase or account. However, analyzing the data collected isn’t always easy, and businesses don’t always know what to do with the data once they have it. That’s where data analytics comes in. Data analytics helps organizations analyze their data and make smarter decisions. There are several benefits of using data analytics in your business. It can help identify problems with your business processes and allow you to find better ways to accomplish your goals. It can also be used to identify patterns, trends, and growth opportunities so that you can focus your resources on these areas to grow your business. Finally, it can be used to reduce risks and improve security.

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Types of Data Analytics

Descriptive – This type of analysis typically involves examining the past in an attempt to predict future outcomes. Businesses often use descriptive analytics to help them better understand their customers and their buying habits. For example, a retailer might analyze customer data to determine which products perform best or what times of the day are most popular with customers. This information can then be used to improve their marketing and operations strategies to drive sales and increase customer satisfaction.

Diagnostic – This type of analysis seeks to identify the root cause of a problem so that it can be addressed. It can also be used to help identify new opportunities that the company might want to pursue. For instance, a company might use diagnostic analytics to find out why its products aren’t selling as well as they expected and discover ways they can address the issue before it becomes a bigger problem.

Predictive – This type of analysis uses historical information to make predictions about the future. It can be used to determine which products are most likely to be successful and to predict when certain events will occur. For example, a company may use predictive analytics to help determine when the best time to launch a new product will be so they can take advantage of the most lucrative sales opportunities.

Prescriptive – This type of analysis identifies the best course of action to take to achieve the desired result. It is often used to help optimize business processes and systems so companies can improve their efficiency and eliminate waste. For example, a business might use prescriptive analytics to identify potential improvements they can make to their supply chain to improve delivery times and reduce expenses.

Analyzing data can help companies make smarter business decisions and help them become more competitive in the marketplace. By analyzing data about the effectiveness of their marketing campaigns, for example, businesses can discover which of their strategies are working and which ones are not. This can help them decide where they should focus their efforts to maximize their return on investment.

Difference between Data Science and Data Analytics with Example

Data Science- It is a broad field of study that focuses on the collection and analysis of large data sets. It is used to solve business problems in a wide range of industries including healthcare, telecommunications, retail, and finance. It also plays an important role in areas such as machine learning and artificial intelligence. For instance, data science algorithms can be used to identify and classify patterns in large amounts of data, which can lead to valuable insights that can be used to create valuable new products and services. It can even help researchers identify possible cures for diseases and other medical conditions such as cancer and heart disease. The average salary for Data Scientists is at $112,622 a year or about $55 per hour.

Data Analytics: it is a type of data analysis that is used to improve the productivity and effectiveness of an organization. It involves using statistics and data mining techniques to analyze large sets of data and extract valuable insights. Data analytics can be used for both strategic and operational purposes and can be used to drive decision-making and optimize business processes. For instance, data analytics can be used to monitor the performance of marketing and sales campaigns and identify areas for improvement or enhancement. It can also be used to monitor customer service and detect trends in customer behavior that can be used to develop new products and improve existing services. In short, data analytics is the science of turning data into actionable insights that can help drive growth and profitability for a business. Data Analysts on average earn $70k to $90k. The demand for Data Analysts is expected to rise over the next decade as more organizations seek to leverage data to make better business decisions and improve their competitive advantage.

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Data Analytics tools for Data Science

Application of Data Analytics

Apply analytical methods to business transactions for the purpose of optimizing performance, reducing costs, and increasing revenue. This may include examining operational processes to identify inefficiencies and opportunities for improvements, reviewing business records to ensure compliance and identify fraudulent activity, analyzing customer data to detect trends and uncover potential growth opportunities, performing market research and analysis to identify new product opportunities and competitors’ strengths and weaknesses, etc. The following steps for Data Analysis can be considered important:

  1. Developing the process/structure of the Analytic solution;
  2. Identifying data needs and their sources;
  3. Preparing data sources for analysis;
  4. Cleaning data for analysis;
  5. Analyzing the data to extract insights and produce actionable information;
  6. Reporting the results and providing recommendations.

Steps of Application of Data Analytics 

Step 1 – Defining the scope of the analysis and developing the process/structure of the analytic solution: The first step involves defining the scope of the analysis and developing the structure and process for the analytic solution. This may involve developing a model of the business process being analyzed and identifying how the process will be optimized using analytic solutions.

Step 2 – Identifying data needs and their sources: Next, an analyst has to identify and capture the relevant transactional and operational data that is required to support the analysis. The type of data required will vary depending on the specific analysis being performed by the analyst. In some cases, the relevant data may be contained in relational databases, spreadsheets, or flat files. In other cases, it may be necessary to develop customized applications to store and analyze the data.

Step 3 – Preparing data sources for analysis: Once the data has been identified and captured, it must be prepared so that it can be used in the analysis. In many cases, the data will need to be restructured and filtered so that it conforms to the standard formats used by the analytical tools that are used to perform the analysis. This step may require the help of subject matter experts who can help make sure that the data is formatted properly for use in a given analysis. The structured and cleansed data can then be used by the analyst in the subsequent steps of the data analysis process.

Step 4 – Cleaning data for analysis: Once the data has been prepared, it may need to be cleaned before it is used in the analysis. This may involve removing any unwanted records that are included as part of the source data so that only the desired records are used in the analysis. For example, if the data is being analyzed using an advanced analytics tool, there may be records that do not contain the desired information because they contain values that are considered outliers. In this case, the records containing these unwanted values can be removed from the data set before it is used to perform the analysis.

Step 5 – Performing data analysis: The final step in the process is to perform the actual analysis using the data and preparing the results for presentation. Depending on the type of analysis being performed, the analysis may be performed manually or using an automated tool such as a spreadsheet application or an advanced analytics tool. In some cases, it may be necessary to combine the results of multiple analyses in order to conduct a complete analysis of the data. For example, the data obtained from the network intrusion detection system can be combined with the results of other analyses to identify the root cause of the breach. The data can then be presented to management so that they can identify the steps that need to be taken to address the risk and prevent future incidents from occurring.

Step 6 – Presenting the Analysis Results: Once the analysis is complete, the results must be presented so that the results can be understood by management and other stakeholders in the organization. The results of the analysis should be presented in a clear and straightforward manner that avoids using complicated terminology or excessive detail. This will make it easier for non-technical members of the organization to understand the results and take appropriate actions based on the findings. Recommendations should also be presented along with a plan of action that will outline the steps that need to be taken based on the findings.

Data Analyst vs Data Scientist Salary

A data analyst is a person who is responsible for gathering and analyzing data for the purpose of supporting decision-making and reporting activities within the organization. The salary of a data analyst will depend on factors such as experience, qualifications, and location. According to Payscale.com, the average annual salary for a data analyst in India is ₹ 3,47,000 per year while in the United States it is $70,100 per year.

By contrast, data scientists are responsible for applying advanced statistical methods to analyze large datasets to uncover hidden patterns and insights that can be used to solve complex business problems and optimize operations. They typically possess a degree in statistics and data science as well as several years of experience working in the field. The average salary of a data scientist ranges from $115,000 to $159,999 per year in the United States depending on the employer and industry while in India it ranges from ₹ 8,14,000 to ₹ 16,33,000 per year depending on the employer and the industry.

Techniques in Data Analytics

There are various analytical methods that can be used for performing data analysis. These data analytics techniques can be explained as follows:

Regression analysis: This is a statistical technique that is used to model the relationship between two or more variables. It is a technique used to examine trends in data and predict outcomes based on existing trends. For example, it can be used to determine the impact of a marketing campaign on product sales.

Factor Analysis: This is an analytical technique that can be used to measure the level of association between different variables in a dataset. It can be used to identify key factors that drive customer satisfaction in a particular company. It can also be used to identify the factors that are likely to impact consumer purchasing decisions in the future.

Cohort Analysis: This is a statistical technique that can be used to analyze the data from different groups of customers or patients in order to understand which factors influence their buying decisions. For example, a company can use cohort analysis to determine which factors are important to the customer age group that is more likely to buy its products.

Monte Carlo Simulations: This technique is a type of stochastic simulation that can be used to create estimates by simulating many different scenarios and analyzing the results of each scenario to predict the likelihood of a particular outcome occurring. For example, it can be used to determine if the company is at risk of failure due to a potential market decline. It can also be used to evaluate the effectiveness of a new product launch or marketing campaign.

Time series analysis: This is a type of statistical analysis that can be used to analyze patterns over time. It can be used to identify seasonal sales patterns that affect customer demand for a particular product. It can also be used to predict market trends in order to determine whether demand for a product is likely to increase or decrease in the near future.

Data Analytics Tools

  • R Programming Language: R is an open-source programming language that can be used for a wide range of data analytics applications. It is widely used by data scientists for performing statistical analysis. It can analyze large datasets quickly and perform sophisticated data analysis tasks with ease.
  • Python Programming Language: Python is another popular programming language that can be used for a variety of applications including data analytics. It is a general-purpose language that is easy to learn and can be used to perform complex data analysis tasks in a relatively short time. Python can be used in both interactive and batch modes for data analytics purposes.
  • SAS Programming Language: SAS stands for Statistical Analysis System and is a software suite that can be used for performing advanced analytics such as data mining and predictive analytics. It is generally used for analyzing data in real time but can also be used for storing and analyzing data in batches as well. It is a commercial product that is available on a subscription basis from SAS Inc. (formerly known as SAS Institute).
  • SQL: SQL is a relational database language that is commonly used for data management and analytics purposes. It is an ANSI standard language and is supported by all major database management systems. It can be used for creating and maintaining databases as well as performing basic and advanced query operations on them.
  • Machine Learning Tools: Machine Learning tools can be used to develop custom models that can be used to make predictions about future trends based on the historical data available. They can incorporate many different techniques including decision trees, regression, clustering, anomaly detection, etc. and they are widely used in industries such as finance, retail, healthcare, manufacturing, transportation, etc.

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Parting Thoughts!!

Hence, from the above blog post, it can be concluded that data analytics plays a very vital role in business organizations today and the tools that are used for performing analytics are constantly evolving and improving with time. This, in turn, makes the data that is analyzed much more reliable and provides better insights which can help businesses make more informed decisions. Individuals aspiring to a technical career in the data science field can pursue various data science and data analytics online courses to gain the necessary skills and expertise required to perform data analytics effectively. They can opt for a Data Analytics course online which will help them gain the needed skills and knowledge required to become a successful data analyst.

 

Author

  • Asmita Kar

    Written by:

    I am a Senior Content Writer working with Pickl.AI. I am a passionate writer, an ardent learner and a dedicated individual. With around 3years of experience in writing, I have developed the knack of using words with a creative flow. Writing motivates me to conduct research and inspires me to intertwine words that are able to lure my audience in reading my work. My biggest motivation in life is my mother who constantly pushes me to do better in life. Apart from writing, Indian Mythology is my area of passion about which I am constantly on the path of learning more.