5 Ways to Become a Data Analyst Complete Guide.pptx
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Are you interested in becoming a Data Analytics person? Do you want to know a number of ways that suggest how you can become a Data Analyst? If your answer is yes, then stay with us till the end. We are going to give you a short piece of information that is true and meets your expectations and helps you to grow more and more. Original Source: https://cetpainfodottech.wordpress.com/2023/01/27/5-ways-to-become-a-data-analyst-complete-guide/
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Data analytics presentation- Management career institute
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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Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
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Organizational Background:3
CRISP-DM(Cross-industry standard process for data mining):3
Data Maturity:4
Role of Data Analyst:6
How Do we Implement the R Platform:6
R Modeling With Regressions and Classifications (TBD)7
Model Performance Evaluation (TBD)8
Visualizations With R (TBD)9
Machine Learning (TBD)10
References11
Introduction to CRISP-DM and the R Platform Organizational BackgroundOrganizational Background:
The organization I currently work for and planning to implement the techniques of the data analytics course is T-Mobile USA, which offers wireless mobile phone services to 0ver 80 million customers in the United States. It’s a huge enterprise with large scale information technology systems that support the business that T-Mobile does. The company is seeing significant growth in terms of business and therefore the IT systems that are supporting the business. Myself as a DEVOPS engineer works on deploying the code to these mission critical systems, host them and operate to make sure the systems are working as expected. As the land scape of our IT systems grow, we want to be able to identify the issues in our systems in advance so that we can prevent them before causing any outage to the business. To achieve such a result, our IT systems logs needs to be analyzed in-depth to unleash the critical insights about the system performance and apply the feedback to improve our systems.
CRISP-DM(Cross-industry standard process for data mining):
The CRISP-DM helps us ensure our data analysis adheres certain standards and CRISP-DM is a proven strategy worldwide. Corporations like IBM have further enhanced and or customized the standard and came up with their own methodology knows as ‘Analytics
Solution
s Unified Method for Data Mining/Predictive Analytics(ASUS_DM)’
The CRISP-DM methodology involves 6 different steps
Business Understanding: Building the knowledge about business requirements and objectives from functional aspect and transforming this knowledge as a data mining objective with an implementation plan.
Data Understanding: Involves the process of data collection from diverse sources of data, review and understand the data to be able to identify the problems which compromise data quality and also give the initial understanding of what the data can deliver.
Data Preparation: The data preparation phase covers all activities to build the final dataset from the initial raw data collected.
Modeling: Modeling techniques are based on the objective of the problem being tried. So, based on the problem, model is decided and based on the model, data is collected.
Evaluation: The evaluation phase is taken up once.
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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.
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In 2023, the job market for data analysts is experiencing unprecedented growth, fueled by the increasing reliance on data-driven decision-making across various industries. Data analysts play a crucial role in extracting valuable insights from vast amounts of data, aiding businesses in making informed choices. If you're considering a career as a data analyst or looking for entry-level opportunities.Visit at: https://www.optnation.com/blog/navigating-the-data-analyst-job-market-in-2023-a-comprehensive-guide/
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1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
Data Science with python is recommended for:
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Following the advice in this learn guide on how to become a data analyst will put you on the right path to being a professional data scientist. No matter what sector you work in, becoming a data analyst is a rewarding path to take. Explore our in-depth learn guide on "How to Become a Data Analyst" to get started with your career if you want to learn more about how to develop a successful career in this sector and discover the numerous courses available to get needed skills and expertise. Learn all the information you require to start your career, including the skills and how to acquire them.
Running head CS688 – Data Analytics with R1CS688 – Data Analyt.docx
Running head: CS688 – Data Analytics with R1
CS688 – Data Analytics with R10
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Introduction to CRISP-DM and the R Platform Organizational Background3
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Data Maturity:4
Role of Data Analyst:6
How Do we Implement the R Platform:6
R Modeling With Regressions and Classifications (TBD)7
Model Performance Evaluation (TBD)8
Visualizations With R (TBD)9
Machine Learning (TBD)10
References11
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The organization I currently work for and planning to implement the techniques of the data analytics course is T-Mobile USA, which offers wireless mobile phone services to 0ver 80 million customers in the United States. It’s a huge enterprise with large scale information technology systems that support the business that T-Mobile does. The company is seeing significant growth in terms of business and therefore the IT systems that are supporting the business. Myself as a DEVOPS engineer works on deploying the code to these mission critical systems, host them and operate to make sure the systems are working as expected. As the land scape of our IT systems grow, we want to be able to identify the issues in our systems in advance so that we can prevent them before causing any outage to the business. To achieve such a result, our IT systems logs needs to be analyzed in-depth to unleash the critical insights about the system performance and apply the feedback to improve our systems.
CRISP-DM(Cross-industry standard process for data mining):
The CRISP-DM helps us ensure our data analysis adheres certain standards and CRISP-DM is a proven strategy worldwide. Corporations like IBM have further enhanced and or customized the standard and came up with their own methodology knows as ‘Analytics
Solution
s Unified Method for Data Mining/Predictive Analytics(ASUS_DM)’
The CRISP-DM methodology involves 6 different steps
Business Understanding: Building the knowledge about business requirements and objectives from functional aspect and transforming this knowledge as a data mining objective with an implementation plan.
Data Understanding: Involves the process of data collection from diverse sources of data, review and understand the data to be able to identify the problems which compromise data quality and also give the initial understanding of what the data can deliver.
Data Preparation: The data preparation phase covers all activities to build the final dataset from the initial raw data collected.
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What is Data analytics? How is data analytics a better career option?
Are you looking for the Best Data analytics Training Institute in Pune Aspire Techsoft offers you the best SAS Data Analytics Certification Training in Pune with Certified expert faculties.
Analytics Organizations & The New Emerging Roles
There are around 72 analytics organizations worldwide. These include 54 analytics consulting companies, 38 analytics product companies, and 9 analytics recruitment companies. The document discusses the roles of big data analyst, data scientist, data engineer, and data visualization analyst. It provides details on the skills required for each role and how they differ while also overlapping at times. It concludes by noting that analytics roles will continue evolving with new technologies and opportunities.
What are Entry Level Data Analyst Jobs?: A Guide Skills
Paid internships and employment training programmes that are directly related to their field of study are permitted for international students holding F-1 student visas, provided that the courses fall under the category of Optional Practical Training to their major subjects of study. You can search for remote data analyst jobs and other OPT positions in the USA with similar specialisations.
Manoj kumar
This document contains a summary profile for Manoj Kumar, an IT professional with over 3 years of experience in fields such as information technology, data warehousing, ETL, BI reporting, and SQL. He is seeking a role that allows him to fully utilize his skills and contribute significantly to his team and organization. His technical skills include SQL, Oracle, BI tools, Python, and Excel. He has worked on projects in various roles for companies such as Progen Business Solutions, JRD Software, and Innovaccer.
Data Analytics Course In Hyderabad-August
Data analytics is widely applied in various fields and industries, including business, healthcare, finance, marketing, sports, and many more. It plays a crucial role in helping organizations make informed decisions, improve operational efficiency, understand customer behavior, identify opportunities, and mitigate risks.
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Take the first step towards a rewarding career in data analytics with APTRON Solutions' Data Analytics Course in Noida. Whether you are a beginner or an experienced professional, our comprehensive training program will empower you to harness the power of data and drive business success. Enroll now and unlock a world of opportunities in the dynamic field of data analytics!
Data Analytics Course In Pune-August
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
For More Details: https://datamites.com/data-analytics-certification-course-training-pune/
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How Much Do Data Scientists Make?
The demand and salary for data scientists tend to be higher than most other ITES jobs. Experience is one of the key factors in determining the salary range of a data science professional.
According to Glassdoor, a Data Scientist in the United States earns an annual average of USD 117,212, and the same site reports that Data Scientists in India make a yearly average of ₹1,000,000.
Data Scientist Career Path
Data Science is currently considered one of the most lucrative careers available. Companies across all major industries/sectors have data scientist requirements to help them gain valuable insights from big data. There is a sharp growth in demand for highly skilled data science professionals who can straddle the business and IT worlds.
The career path to becoming a data scientist isn’t clearly defined since this is a relatively new profession. People from different backgrounds like mathematics, statistics, computer science or economics, end up in data science.
The major designations for data science professionals are:
Data Analyst
Data Scientist (entry-level)
Associate data scientist
Data Scientist (senior-level)
Product Manager
Lead data scientist
Director/VP/SVP
That was all about Data Scientist Job Description.
Become a Data Scientist Today!
In this write-up, we covered the Data Scientist job description in detail. Irrespective of which location you are in, there is no dearth of jobs for skillful data scientists. A career in data science is a rewarding journey to embark on, especially in the finance, retail, and e-commerce sectors. Jobs are also available with Government departments, universities and research institutes, telecoms, transports, the list goes on.
This video covers
Introductory Questions
Data Science Introduction
Data Science Technical Interview QnA :
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#MachineLearning
#DataAnalyticstechnical Interview
#DataScienceProjects
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What is the difference between Data Science and Data Analytics.pdf
This article explores the distinction between data science and data analytics, highlighting the contrasting roles and methodologies employed in these two fields, helping readers gain a clear understanding of their unique contributions to the world of data.
Data Analytics Course In Delhi-August
Data analytics courses are educational programs designed to teach individuals the skills and techniques needed to work with data, analyze it, and extract meaningful insights.
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data science and business analytics
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
Your Guide to Becoming a Data Analyst
Apart from being good at the technical requirements of Data Analyst jobs of data handling, data management, and data analysis. They must be good at communication, problem-solving, and critical decision-making, and they must have good financial knowledge. They process the data and visualize the data using statistical and mathematical approaches. visit at - https://www.optnation.com/blog/your-guide-to-becoming-a-data-analyst/
Introduction to Data Analytics
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
Business Analytics
Business analytics helps organizations make informed decisions by deriving insights from data. While computers can process and analyze large amounts of data, humans play important roles in business analytics by understanding context, asking relevant questions, and interpreting results. Effective human analysts have skills in data analysis, statistics, communicating insights, and critical thinking. It is also important to acknowledge potential human bias and mitigate it through diverse teams and transparent processes.
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- 1. 5 Ways to Become a Data Analyst : Complete Guide
- 2. Table Of Contetn The Practice of Mathematics and Programming Effective Communication Aware of Data Processing Tools and Platforms Logical and Critical thinking
- 3. A Data Analyst is a person who works on collecting, organizing, and analyzing data in a manner. The data may be of different types either structured, unstructured or other, and have the ability to present the entire data in the form of graphs in order to show precise results. Several Ways to Become a Data Analyst
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- 5. It is mandatory to have good communication and listening skills while meeting with clients and also connecting with other team members. This will play a major and effective role in the personality part and even works as a respectful person in a current organization and everything you need to know about data analyst. Effective Communication
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- 7. Logical and Critical thinking Taking out the correct evaluations and functions, a person should have the ability to think critically and logically in a particular current situation. These skills will create a deeper meaning and will work on database management more properly. These are the 4 ways that can help a person in becoming a Data Analyst. Other than that, another way that has a deeper meaning is a small number of steps that relate to qualification and roadmap.
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