How To Become Data Analytics

Analytics?is the systematic computational analysis of data or statistics.?It is used for the discovery, interpretation, and communication of meaningful patterns in?data. It also entails applying data patterns towards effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of?statistics,?computer programming?and?operations research?to quantify performance

where you can apply analytics ?

Marketing optimization, Marketing organizations use analytics to determine the outcomes of campaigns or efforts, and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.

Risk analytics Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers.?Credit scores?are built to predict an individual's delinquency behavior and are widely used to evaluate the credit worthiness of each applicant. Furthermore, risk analyses are carried out in the scientific world?and the insurance industry

Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, predictions, and automation

Security analytics refers to information technology (IT) to gather security events to understand and analyze events that pose the greatest risk. Products in this area include?security information and event management?and user behavior analytics.

People analytics uses behavioral data to understand how people work and change how companies are managed. People analytics is also known as workforce analytics, HR analytics, talent analytics, people insights, talent insights, colleague insights, human capital analytics, and HRIS analytics. HR analytics is the application of analytics to help companies manage?human resources. Additionally, HR analytics has become a strategic tool in analyzing and forecasting Human related trends in the changing labor markets, using Career Analytics tools. The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems

MISUNDERSTANDING BETWEEN TWO TERMINOLOGIES?

Business analytics?(BA) refers to the skills, technologies, and practices for continuous iterative exploration and investigation of past?business?performance to gain insight and drive business planning.?Business analytics focuses on developing new insights and understanding of business performance based on?data?and?statistical methods. In contrast,?business intelligence?traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning. In other words,?business intelligence?focusses on description, while business analytics focusses on prediction and prescription.

History

Analytics have been used in business since the management exercises were put into place by?Frederick Winslow Taylor?in the late 19th century.?Henry Ford?measured the time of each component in his newly established assembly line. But analytics began to command more attention in the late 1960s when computers were used in?decision support systems. Since then, analytics have changed and formed with the development of?enterprise resource planning?(ERP) systems,?data warehouses, and a large number of other software tools and processes. In later years the business analytics have exploded with the introduction of computers. This change has brought analytics to a whole new level and has brought about endless possibilities. As far as analytics has come in history, and what the current field of analytics is today, many people would never think that analytics started in the early 1900s with Mr. Ford himself.

Business intelligence?(BI) comprises the strategies and technologies used by enterprises for the?data analysis?and management of business?information.?Common functions of business intelligence technologies include?reporting,?online analytical processing,?analytics,?dashboard?development,?data mining,?process mining,?complex event processing,?business performance management,?benchmarking,?text mining,?predictive analytics, and?prescriptive analytics.

BI technologies can handle large amounts of structured and sometimes unstructured data to help identify, develop, and otherwise create new strategic?business opportunities. They aim to allow for the easy interpretation of these?big data. Identifying new opportunities and implementing an effective strategy based on?insights?can provide?businesses?with a competitive market advantage and long-term stability

History

The earliest known use of the term?business intelligence?is in Richard Millar Devens'?Cyclop?dia of Commercial and Business Anecdotes?(1865). Devens used the term to describe how the banker?Sir Henry Furnese?gained profit by receiving and acting upon information about his environment, prior to his competitor.

The ability to collect and react accordingly based on the information retrieved, Devens says, is central to business intelligence. When?Hans Peter Luhn, a researcher at?IBM, used the term?business intelligence?in an article published in 1958, he employed the?Webster's Dictionary?definition of intelligence: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.

Compared with business analytics

Business intelligence and?business analytics?are sometimes used interchangeably, but there are alternate definitions.?Thomas Davenport, professor of information technology and management at?Babson College?argues that business intelligence should be divided into?querying,?reporting,?Online analytical processing?(OLAP), an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI focusing on statistics, prediction, and optimization, rather than the reporting functionality

other terminologies important to know

Behavioral analytics?is a recent advancement in?business analytics?that reveals new insights into the behavior of consumers on?eCommerce?platforms, online games, web and mobile applications, and?IoT. The rapid increase in the volume of raw event data generated by the digital world enables methods that go beyond typical analysis[promotional language]?by demographics and other traditional metrics that tell us what kind of people took what actions in the past. Behavioral analysis focuses on understanding how consumers act and why, enabling accurate predictions about how they are likely to act in the future.

Big data?is a field that treats ways to analyze, systematically extract information from, or otherwise deal with?data sets?that are too large or complex to be dealt with by traditional?data-processing?application software. Data with many fields (columns) offer greater?statistical power, while data with higher complexity (more attributes or columns) may lead to a higher?false discovery rate.?Big data analysis challenges include?capturing data,?data storage,?data analysis, search,?sharing,?transfer,?visualization,?querying, updating,?information privacy, and data source.

Artificial intelligence marketing?(AIM) is a form of?marketing?leveraging?artificial intelligence?concept and model such as?machine learning?and?Bayesian Network?to achieve marketing goals. The main difference resides in the?reasoning?part which suggests it is performed by computer and algorithm instead of a human.

Dashboard?is a type of?graphical user interface?which often provides at-a-glance views of?key performance indicators?(KPIs) relevant to a particular objective or business process. In other usage, "dashboard" is another name for "progress report" or "report" and considered a form of?data visualization.

Data mining?is a process of extracting and discovering patterns in large?data sets?involving methods at the intersection of?machine learning,?statistics, and?database systems.?Data mining is an?interdisciplinary?subfield of?computer science?and?statistics?with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use

Learn important data analytics skills

• BOOT CAMP

Case Western Reserve University Online Data Analytics Boot Camp

CWRU Data Analytics Boot Camp is a rigorous, part-time program that prepares students with the fundamental skills for data analytics and visualization. Through hands-on, in-person instruction, you\’ll cover a wide range of topics and graduate ready to apply your skills in the workforce.

Columbia Engineering Online Data Analytics Boot Camp

Are you ready to become a data-driven professional? Columbia Engineering Data Analytics Boot Camp is a challenging, part-time bootcamp that equips learners with the specialized skills for data analytics and visualization through hands-on, in-person classes.

University of California, Berkeley Online Data Analytics Boot Camp

Turn data into actionable insights. Berkeley Data Analytics Boot Camp is a dynamic, part-time program that covers the in-demand tools and technologies for data analytics and visualization through rigorous, project-based classes.

Georgia Institute of Technology Online Data Science and Analytics Bootcamp

Expand your skill set and grow as a data scientist. Georgia Tech Data Science and Analytics Boot Camp covers the skills needed to analyze and solve complex data analytics and visualization problems.

USC Viterbi Affiliated with Trilogy Education Services OnlineData Analytics Boot Camp

Expand your skill set and grow as a data analyst. This program covers the specialized skills to be successful in the field of data in 24 weeks.

• ONLINE PROFESSINAL CERTIFICATE

1. MICROSOFT EXCEL

Microsoft Office Specialist: Microsoft Excel Expert (Excel and Excel 2019)

Demonstrate that you have the skills needed to get the most out of Excel by earning the Microsoft Office Specialist: Excel Expert Certification. This certification demonstrates competency in creating, managing, and distributing professional spreadsheets for a variety of specialized purposes and situations.

An individual earning this certification has approximately 150 hours of instruction and hands-on experience with the product, has proven competency at an industry expert-level and is ready to enter into the job market. They can demonstrate the correct application of the principal features of Excel at an expert-level and can complete tasks independently.

2. Learn Python

Gain the career-building Python skills you need with DataCamp’s online training. Through hands-on learning you’ll discover how this versatile programming language is used by the world’s largest companies for everything from building web applications to data science and machine learning.

3.Learn Power?BI

Learn new skills with Microsoft Power?BI training. Our hands-on guided-learning approach helps you meet your goals quickly, gain confidence, and learn at your own pace.

4.R

R is a widely used statistical programming language that’s beloved by people in academia and the tech industry. But that makes it sound more intimidating than it actually is. R is a great first language for anyone interested in answering questions with data analysis, data visualization, and data science.

Skills Required for Data Analysts

• Programming Languages (R/SAS): data analysts should be proficient in one language and have working knowledge of a few more. Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.
• Creative and Analytical Thinking: Curiosity and creativity are key attributes of a good data analyst. It’s important to have a strong grounding in statistical methods, but even more critical to think through problems with a creative and analytical lens. This will help the analyst to generate interesting research questions that will enhance a company’s understanding of the matter at hand.
• Strong and Effective Communication: Data analysts must clearly convey their findings — whether it’s to an audience of readers or a small team of executives making business decisions. Strong communication is the key to success.
• Data Visualization: Effective data visualization takes trial and error. A successful data analyst understands what types of graphs to use, how to scale visualizations, and know which charts to use depending on their audience.
• Data Warehousing: Some data analysts work on the back-end. They connect databases from multiple sources to create a data warehouse and use querying languages to find and manage data.
• SQL Databases: SQL databases are relational databases with structured data. Data is stored in tables and a data analyst pulls information from different tables to perform analysis.
• Database Querying Languages: The most common querying language data analysts use is SQL and many variations of this language exist, including PostreSQL, T-SQL, PL/SQL (Procedural Language/SQL).
• Data Mining, Cleaning and Munging: When data isn’t neatly stored in a database, data analysts must use other tools to gather unstructured data. Once they have enough data, they clean and process through programming.
• Advanced Microsoft Excel: Data analysts should have a good handle on excel and understand advanced modeling and analytics techniques.
• Machine Learning: Data analysts with machine learning skills are incredibly valuable, although machine learning is not expected skill of typical data analyst jobs.

What tools do data analysts use?

• Google Analytics (GA): GA helps analysts gain an understanding of customer data, including trends and areas of customer experience that need improvement on landing pages or calls to action (CTAs)
• Tableau: Analysts use Tableau to aggregate and analyze data. They can create and share dashboards with different team members and create visualizations
• Jupyter Notebook system: Jupyter notebooks make it simple for data analysts to test code. Non-technical folks prefer the simple design of jupyter notebooks because of its markdown feature
• Github: Github is a platform for sharing and building technical projects. A must for data analysts who use object-oriented programming
• AWS S3: AWS S3 is a cloud storage system. Data analysts can use it to store and retrieve large datasets

Is Coding Required to be a Data Analyst?

Some data analysts are proficient in programming languages while others may use analytics software or Excel to analyze data and provide insights. Whether or not coding is required for a data analyst typically depends on the job or the employer. Employers may or may not list programming as a required skill for data analysts in job listings. It is important to look at the job description and consider your background before applying.