Data analytics is the science of analyzing raw data. Companies use data analytics to draw meaningful conclusions about the masses of information they gather about their customers every day. It helps them to reveal trends, produce metrics, and find the answers to burning questions that wouldn’t even have an answer without today’s sophisticated analysis software. These insights are then used to enhance the overall efficiency of a business or system.

With the continuing digitization of the modern world, demand for data analysts is growing fast. According to studies, almost 70% of employers in the U.S. say they’ll prefer candidates with data skills by 2021.

This article explores some of the different aspects of data analytics and the skills and responsibilities involved in the field. You’ll come away with a better understanding of the role of a data analyst and the different pathways to entering this exciting new area of tech.

How Is Data Analytics Used in The Real World?

Below is a handful of examples of how data analytics is used by companies every day:

Types of Data Analytics

Data analytics is broadly categorized into four types:

  1. Descriptive analytics explains happenings in a given period, like the number of views on a site or product. This is useful for establishing comparisons and trends to help inform business decisions.
  2. Predictive analytics looks at what is going to happen in the near term. For example, how many weather models predict a chilly winter this year, or what happened to sales last year during a particularly cold winter.  
  3. Diagnostic analytics aims to explain the causes behind a happening. This involves a bit of hypothesizing, like exploring whether cold weather or poor marketing affects cola sales the most.
  4. Prescriptive analytics prescribes a course of action. For example, if a manufacturing company is producing 100 units of a product, and the demand over a period of a few months is expected to rise to 110 units, a prescriptive analysis would advise the company to increase its production capacity by 10 units.

Role of a Data Analyst

The basic duty of a data analyst is to improve performance and efficiency by finding patterns in data. The main steps involved are data mining, data management, statistical analysis, and data presentation.  

Data mining involves sorting meaningful data from unstructured data sources, which may include written text, large complex databases, or raw sensor data. The main steps in the process are ETL (which stands for extract, transform, and load). These steps help convert raw data into a useful format, which prepares the data for later storage and analysis.

Data management or warehousing involves designing and implementing databases that allow easy access to the results of data mining. It involves creating and managing SQL databases.

Statistical analysis is the actual process of creating insights from data. Statistics and machine learning techniques are used to create statistical models that reveal trends in the data. Programming languages like R or Python are required for this process.

Data presentation is the final step, which allows the generated data insights to be shared with stakeholders. The most important aspect of data presentation is data visualization. Compelling visualizations tell the crux of the story in the data, which in turn helps managers understand the importance of these insights.

Skills Required to become a Data Analyst

Data professionals typically require the same foundational skills to master in any field related to data science. These typically consist of:

Statistics – Strong fundamentals in probability and statistics is a must for any data analyst.

Microsoft Excel – Excel makes data management hassle-free and convenient. On top of the traditional spreadsheets, a data analyst should have knowledge of Visual Basic and Macros.  

SQL (Structured Query Language) –SQL is a specialized but easy to learn programming language. SQL is capable of managing large databases that Excel can’t handle. It’s one of the most important and ubiquitous skills in any industry dealing with software or data science.

R and Python – R and Python are the two most important statistical programming languages used in data science. They’re used to perform advanced analyses with precision and speed, so they’re rated higher than SQL and Excel. They’re commonly used for predictive analysis, data collection, and web scraping. Both R and Python are open-source and equally valued amongst employers. A deep understanding of either language will give you an advantage when sending your cover letter and portfolio to potential employers.

Data Visualization skills – The ability to visualize and communicate insights related to your company's service is an important skill set for any data analyst. Tableau is a popular visualization software amongst data analysts. Some data analysts might also need web development skills and knowledge of machine learning, as well as a critical and analytical mindset.

Traditional Learning Options for Data Analytics

Recruiters looking to fill an entry-level data analyst position will always look favorably on candidates who have completed a data analytics program with a reputable institution. Even a degree in statistics, economics, or mathematics is suitable for a starter-level position as a data analyst. You can then build your knowledge on the job and add skills like SQL, Python, or R to your portfolio. Experienced data analysts often make double or more than an entry-level data analyst. Below is a list of data science programs offered by universities in the US:

1. Carnegie Mellon University

Course: Master of Computational Data Science

Duration: 2 years

Location: Pittsburgh, Pennsylvania

Core courses: Machine Learning, Cloud Computing, and Data Science Seminar

Tracks available: Systems and Human-Centered Data Science

2. Stanford University

Course: M.S. in Statistics and Data Science

Duration: 2 years

Location: Stanford, California

Core courses: Numerical Linear Algebra, Discrete Mathematics and Algorithms, Optimization, Stochastic Methods in Engineering or Randomized Algorithms and Probabilistic Analysis, Introduction to Statistical Inference, Introduction to Regression Models

3. Georgia Institute of Technology

Course: Master of Science in Analytics

Duration: 1 year

Location: Atlanta, Georgia

Core courses: Big Data Analytics in Business and Visual Analytics

Tracks available:  Computational Data Analytics

4. University of Washington

Course: Master of Science in Data Science

Duration: 2 years

Location: Seattle, Washington

Core courses: Introduction to Statistics & Probability, Information Visualization, Applied Statistics & Experimental Design, Data Management for Data, Software Design for Data Science, Scalable Data Systems & Algorithms, and Human-Centered Data Science.

5. Columbia University

Course: Master of Science in Data Science

Duration: 1.5 years

Location: New York City, New York

Core courses: Algorithms for Data Science, Statistical Inference and Modelling, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization

6. New York University

Course: Master of Science in Data Science

Duration: 2 years

Location: New York City, New York

Core courses: Introduction to Data Science, Probability and Statistics for Data analysis

Tracks available: Data Science, Data Science Big Data, Data Science Natural Language Processing, Data Science Physics, and Data Science Biology

Data Analytics Online Courses

If the time and expense of a college degree is not for you, there are many short-term certification courses available online. Some of the popular courses in data science and analytics are as follows:

  1. At Thinkful, We provide a highly rated data science course. The full-time option is an accelerated, 5-month, online course. You can also choose to learn part-time in a 6-month program that offers the same curriculum and learning support. The best part? You don’t have to pay any tuition til you get hired and earn $40,000 a year.
  2. edX has several professional certifications and micro-master programs in different fields of data analytics, like probability and statistics. Most of these programs are part-time, for both beginners and intermediate levels. They’re backed by companies like Microsoft and IBM, and universities like MIT and Harvard.
  3. Coursera offers several highly rated courses in conjunction with reputable universities and companies like IBM and Google. These courses are also part-time and completely online. They can take from a few months to a year to complete, along with professional certifications.
  4. Data Council of America (DASCA) offers different certifications for data analysts. It tests candidates along with the world's best robust generic Big Data knowledge framework before awarding the certification. Being certified by DASCA will give you a huge advantage in the job market.

Other courses and bootcamps worth checking out are offered by Ironhack, Simpli Learn, and Edureka.  

Self-Learning Options for Data Analytics

If you want to take a stab at teaching yourself the basics before investing in a course, you’ve got options. Aside from the traditional learning pathways, you can always source textbooks, video lectures and podcasts on data science, as well as free and paid courses on coding languages, probability, and statistics. There’s also huge benefit to be gained from joining discussion groups and tech threads on social media (your fellow data nerds are out there, you just gotta find them). And if you’re already working but want to transition from a different field, many organizations offer free courses on data science for employees so they can hone their skills.

Be it self-learning, an online certification, or a formal educational program, you’ll need to have a strong foundation in statistics and coding to become a successful data scientist—so it’s never too early to start preparing.

Kickstart Your Career in Data Analytics

Data analytics is a highly rewarding career choice and there are many different pathways into the field. We hope this article has helped shed some light on the many learning options available to you. If you’d like to discuss your future in data with a real person, schedule a call with us. We’ll be happy to help.

For further inspiration, don’t forget to check out our data analytics blog. We cover a range of different data analytics topics that you might find useful as you venture into your new tech career.

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