- Introduction
- What is Data Analytics?
- Why is Data Analytics important?
- What are the five steps to analyzing data?
- The Core Mathematical Knowledge for Data Analytics
- Statistical Inference for Data Analytics
- Optimal Decision Making for Data Analytics
- Probabilistic Modeling for Data Analytics
- Data Mining for Data Analytics
- Data Visualisation for Data Analytics
- The Future of Data Analytics
- Where can Data Analytics take you?
- Get Started with Statype

Here at Statype, we are focused on fostering collaboration, innovation, and world-class knowledge of data analytics and its many applications. There are new opportunities each day to apply computational and quantitative thinking to innovative lines of inquiry.

Let’s dive in - there’s exciting work to do!

As more data becomes available and the computational capacity increases, our economy, society, and daily life will become even more dependent on our ability to systematically learn from this data at great speeds.

In this guide, we will go over some of the concepts involved in data analytics, including core mathematical knowledge, statistical inference, decision making theory, probabilistic modeling, data mining, data visualisation, and the future of data analytics.

Data analytics converts raw data into actionable insights. It includes a range of tools, technologies, and processes used to find trends and solve problems by using data. Data analytics can shape business processes, improve decision-making, and foster business growth.

Data analytics helps businesses gain more visibility and a deeper understanding of their processes and services. It gives them detailed insights into the customer experience and customer problems. By shifting the paradigm beyond data to connect insights with action, companies can create personalized customer experiences, build new and improved products, optimize operations, and increase productivity.

The current COVID-19 pandemic has shown that businesses that are “insight-driven” show much higher resilience and are able to tighten their dominant market positions, despite turbulent conditions. These businesses are equipped to manage the crisis better and are expected to recover and excel faster once markets recover.

Having data and analytics at their core, insight-driven organizations are prepared to make the best decisions in an efficient manner. It enables them to manage core business operations in the most cost-effective way and react on a day-to-day basis.

There are five core steps involved in the process of analyzing data. We’ll go into detail with each of the five steps next:

- Data collection – This includes identifying data sources and collecting data from them. Data collection follows ETL or ELT processes.
- Data Storage – Data is moved to long-term storage to enable historical analysis and merging of datasets
- Data processing – When data is in place, it has to be converted and organized to obtain accurate results from analytical queries.
- Data cleansing – Data cleansing involves scrubbing for any errors such as duplications, inconsistencies, redundancies, or wrong formats. It’s also used to filter out any unwanted data for analytics.
- Data analysis – This is the step in which raw data is converted to actionable insights. The following are four types of data analytics: Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analytics.

The fundamental pillars of mathematics that you will use daily in data analysis are arithmetic, linear algebra, probability, and descriptive statistics.

- Arithmetic – the branch of mathematics dealing with the properties and manipulation of numbers
- Linear algebra – a mathematical discipline that deals with vectors and matrices and, more generally, with vector spaces and linear transformations
- Probability – the branch of mathematics concerning the occurrence of a random event, and four main types of probability exist: classical, empirical, subjective, and axiomatic.
- Descriptive Statistics – describe, show, and summarize the basic features of a dataset found in a given study, presented in a summary that describes the data sample and its measurements.

- Probability Theory – the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data analysis.
- Decision Theory – a branch of applied probability theory and analytic philosophy concerned with the theory of making decisions based on assigning probabilities to various factors and assigning numerical consequences to the outcome. There are three branches of decision theory: Normative Decision theory, Prescriptive Decision Theory, and Descriptive Decision Theory.
- Point and Interval Estimation – Point and Interval estimates are the two forms of population parameter estimation based on sample data.
- Tests of Hypotheses – Hypothesis Testing is a type of statistical analysis in which you put your assumptions about a population parameter to the test. It is used to estimate the relationship between statistical variables.
- Bayesian Analysis – a method of statistical inference in which Bayes’ theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Allows one to combine prior information about a population parameter with evidence from information contained in a sample to guide the statistical inference process.
- Maximum Likelihood Estimation – a method that determines values for the parameters of a model. The parameter values are found such that they maximise the likelihood that the process described by the model produced the data that were actually observed.

A decision that leads to at least as good a known or expected outcome as all other available decision options. It is an important concept in decision theory. In order to compare the different decision outcomes, one commonly assigns a utility value to each of them.

A statistical technique used to take into account the impact of random events or actions in predicting the potential occurrence of future outcomes.

Data mining is used to discover patterns and relationships in data. The process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis. Data mining techniques and tools enable enterprises to predict future trends and make more-informed business decisions.

Employees and business owners at every level need to have an understanding of data and of its impact. That’s where data visualization comes in handy. With the goal of making data more accessible and understandable, data visualization in the form of dashboards is the go-to tool for many businesses to analyze and share information.

The “new normal” will require strong analytical competencies to harness the value of data for improved collaboration, efficiency, and effectiveness. Companies must focus on evolving their analytical maturity in addition to developing capabilities around rapid experimentation and trial and error. Remaining agile will be essential for handling this “new normal.”

Pursue your passions in a variety of positions, for careers in a range of industries that require quantitative work.

Reach out to us via email at contact@statype.com.

Tammy Butow

VP of Product

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