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Data analytics presentation- Management career institute

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Data analytics presentation- Management career institute

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1. The basic definition of Data, Analytics, and Data Analytics

2. Definition: Data: Data is a set of values of qualitative or quantitative variables. It is information in the raw or unorganized form. It may be a fact, figure, characters, symbols etc

Analytics: Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making.

Data Analytics: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.

3.Types of analytics: Predictive Analytics (What could happen?)
Prescriptive Analytics (What should we do)
Descriptive Analytics (What has happened?)

4.Why Data analytics? Data Analytics is needed in Business to Consumer applications (B2C)

5.The process of Data analytics: Data requirements,
Data collection, Data processing, Data cleaning, Exploratory data analysis,
Modeling and algorithms, Data product, Communication

6.The scope of Data Analytics: Bright future of data analytics, many professionals and students are interested in a career in data analytics.

7.Importance of data analytics:1. Predict customer trends and behaviors
Analyze,
2 interpret and deliver data in meaningful ways
3.Increase business productivity
4.Drive effective decision-making

8.why become a data analyst? talented gaps of skill candidates, good salaries for freshers, great future growth path



9. What recruiters look for in applicants: Problem-Solving Skills, Analytical Mind, Maths and Statistic Skills, Communication (both oral and written), Teamwork Abilities

10. Skill is required for Data analytics?
1.) Analytical Skills
2.) Numeracy Skills
3.) Technical and Computer Skills
4.) Attention to Details
5.) Business Skills
6.) Communication Skills

11. Data analytics tools
1.SAS: SAS (Statistical Analysis System) is a software suite developed by SAS Institute. sas language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis. The language has the ability to read data from databases and common spreadsheets.

2. R: R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.

3.PYTHON: Python is a popular programming language Python is a powerful, flexible, open-sources language that is easy to use,
and has a powerful library for data manipulation and analysis.

4.TABLEAU: Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.

1. The basic definition of Data, Analytics, and Data Analytics

2. Definition: Data: Data is a set of values of qualitative or quantitative variables. It is information in the raw or unorganized form. It may be a fact, figure, characters, symbols etc

Analytics: Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making.

Data Analytics: Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain.

3.Types of analytics: Predictive Analytics (What could happen?)
Prescriptive Analytics (What should we do)
Descriptive Analytics (What has happened?)

4.Why Data analytics? Data Analytics is needed in Business to Consumer applications (B2C)

5.The process of Data analytics: Data requirements,
Data collection, Data processing, Data cleaning, Exploratory data analysis,
Modeling and algorithms, Data product, Communication

6.The scope of Data Analytics: Bright future of data analytics, many professionals and students are interested in a career in data analytics.

7.Importance of data analytics:1. Predict customer trends and behaviors
Analyze,
2 interpret and deliver data in meaningful ways
3.Increase business productivity
4.Drive effective decision-making

8.why become a data analyst? talented gaps of skill candidates, good salaries for freshers, great future growth path



9. What recruiters look for in applicants: Problem-Solving Skills, Analytical Mind, Maths and Statistic Skills, Communication (both oral and written), Teamwork Abilities

10. Skill is required for Data analytics?
1.) Analytical Skills
2.) Numeracy Skills
3.) Technical and Computer Skills
4.) Attention to Details
5.) Business Skills
6.) Communication Skills

11. Data analytics tools
1.SAS: SAS (Statistical Analysis System) is a software suite developed by SAS Institute. sas language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis. The language has the ability to read data from databases and common spreadsheets.

2. R: R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows, and Mac.

3.PYTHON: Python is a popular programming language Python is a powerful, flexible, open-sources language that is easy to use,
and has a powerful library for data manipulation and analysis.

4.TABLEAU: Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.

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Data analytics presentation- Management career institute

  1. 1. DATA ANALYTICS
  2. 2. DATA? In computing, data is information that has been translated into a form that is efficient for movement or processing.Data can exist in a variety of forms as numbers or text on pieces of paper, as bits and bytes stored in electronic memory, or as facts stored in a person's mind.
  3. 3. ANALYTICS? Analytics is the discovery, interpretation, and communication of meaningful patterns in data and applying those patterns towards effective decision making .Analytics is an encompassing and multidimensional field that uses mathematics, statistics, predictive modeling and machine learning techniques to find meaningful patterns and knowledge in recorded data.
  4. 4. Types of Analytics 1 2 3 Analytics Prescriptive Analytics Enabling smart decisions based on data What should we do Descriptive Analytics Mining data to provide business insights? What has happened? Predictive Analytics predicting the future based on historical patterns What could happen?
  5. 5. Types of Analytics
  6. 6. What is DATA analytics? Data analysis is a process of inspecting, cleansing, transforming, and modeling data. Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain
  7. 7. Why Data Analytics Data Analytics is needed in Business to Consumer applications (B2C). Organisations collect data that they have gathered from customers, businesses, economy and practical experience. Data is then processed after gathering and is categorised as per the requirement and analysis is done to study purchase patterns and etc.
  8. 8. The process of Data Analysis Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. There are several phases that can be distinguished :Data requirements, Data collection ,Data processing ,Data cleaning, Exploratory data analysis, Modeling and algorithms , Data product ,Communication
  9. 9. Scope of Data Analytics Bright future of data analytics, many professionals and students are interested in a career in data analytics. Any person who likes to work on numbers, has a logical thinking, can understand figures and can turn them into actionable insights, has a good future in this field. A proper training of the tools of data analytics would be required to begin with. Since it is a course that requires effort to learn and get certified, there is always dearth of qualified professionals. Being a relatively new field also, the demand for such professionals is more than the current supply. Higher demand also means higher salaries.
  10. 10. Importance Data Analytics ● Predict customer trends and behaviours ● Analyse, interpret and deliver data in meaningful ways ● Increase business productivity ● Drive effective decision-making
  11. 11. Job opportunities in Data An
  12. 12. Data analytics job title
  13. 13. Salary Job title
  14. 14. Demand of Data Analysis Jobs Data analysis jobs are everywhere and they are bound to increase! Here are some facts and figures to highlight this: “2.5 billion gigabytes (GB) of data was generated every day in 2012. (IBM) International Business Machines”
  15. 15. Going by the statistics, by 2020, about 1.7 megabytes of new information will be created every second for every human! Well, that’s huge. Very huge. Companies will need more and more data specialists to analysis and manage the data generated.
  16. 16. Basic Skills required to start your career in data analytics ● How to set up data structure? ● How to create data visualizations ● Knowledge with database languages like SQL, MySQL ● knowledge of big data tools like Hive or Pig ● Know statistical programming languages like R or Python ● Understanding of machine learning tools & techniques
  17. 17. What recruiters look for in applicants Problem Solving Skills: When working with complex sets of data, companies rely on analysts to interpret the numbers and figures to find solutions to their problems. Your primary job is to read between the numbers and datasets to find the answers that inexperienced analysts can't see. You are who they turn to when they need a complex problem solved with data, and you could potentially shape the future of the company. Analytical Mind: This goes hand-in-hand with the problem-solving skills needed. An optimal candidate for any analytics position must have a mind that naturally looks for answers and connections between data sets. This is incredibly useful, especially when handling large sets of data. You must be able to decipher and make connections that nobody else can.
  18. 18. Maths and Statistic Skills:It goes without saying that if you want to be an effective data analyst or scientist, you must be able to do the math to analyze and interpret the data. Although a majority of calculations are completed with computer programs, a solid foundation and understanding of mathematics or statistics will take you far in this field. Communication (both oral and written): Once you find solutions and make connections using the data you won't be keeping it to yourself. You must be able to succinctly and accurately explain sophisticated mathematical and statistical principles that other departments can understand.Communication skills go a long way in any career and data analysis is no exception. Teamwork Abilities: More times than not, you never work alone. You will be a part of a team of data specialists, and it is vital to the success of the team and organization that you can all work together to solve complex problems.
  19. 19. Skill is required for Data analytics ? 1.) Analytical Skills 2.) Numeracy Skills 3.) Technical and Computer Skills 4.) Attention to Details 5.) Business Skills 6.) Communication Skills
  20. 20. CAREER Data analysis is a rapidly growing field and highly skilled analysts in increased demand across all sectors. This is evident from the average salary of a data analyst in India. This implies that you would find many opportunities but you will still have to be outstanding and exhibit excellent data analytics skills to be successful as a data analyst.
  21. 21. Top companies hiring for business Analytics
  22. 22. What are The Top 4 Roles To Data Analyst
  23. 23. A data scientist is a person who utilises the data in possession of the organisation to design business-oriented learning models and types of machinery.
  24. 24. Data analytics role expects you to prepare insights from the available data which directly impacts decisions in businesses. There is direct involvement of data analysts in everyday business activities.
  25. 25. The Truth About Data Analytics: Data analytics for businesses that want to make good use of the data that they are taking in. Businesses that can use data analytics properly are more likely than others to succeed and thrive. But with all of the advantages of data analytics, the key benefits can be described in this way: ● Data analytics reduces the costs associated with running a business. ● It cuts down on the time needed to come to strategy-defining decisions. ● Data analytics help to more-accurately define customer trends. Determining the Effectiveness of Your Analytics Program Given the growing familiarity and popularity of data analytics, there are a number of advanced analytics programs available on the market. As such, there are certain traits to look for in any analytics solution that will help you gage just how effective it will be in improving your business.
  26. 26. Data analytics in the banking sector
  27. 27. Data Analytics Tools .
  28. 28. SAS (Statistical Analysis System) is a software suite developed by SAS institute. SAS language can be defined as a programming language in the computing field. This language is generally used for the purpose of statistical analysis.The language has an ability to read the data from databases and common spreadsheets. The output of the statistical analysis performed is represented in the form of graphs. The data is given in the form of RTF, PDF, and HTML files. The compilers, under which the SAS programming language runs, can be used on a wide variety of platforms, including Linux, mainframe computers, Microsoft Windows, and a number of other UNIX.
  29. 29. R is a programming language and software environment for statistical analysis, graphics representation and reporting.R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. Features of R The following are the important features of R − ● R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities. ● R has an effective data handling and storage facility, ● R provides a suite of operators for calculations on arrays, lists, vectors and matrices. ● R provides a large, coherent and integrated collection of tools for data analysis. ● R provides graphical facilities for data analysis and display either directly at the computer or
  30. 30. Python is a popular programming language python is a powerful,flexible,open-sources language that is easy to use, and has a powerful libraries for data manipulation and analysis.
  31. 31. Tableau Software is a software company that produces interactive data visualization products focused on business intelligence.

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