I have had the opportunity to work for some of the greatest technology companies in the industry. I will always owe a debt of gratitude for opportunities at Cognos, Microsoft, and IBM. I began working in the analytics space in the summer of 2000 after a short career as a systems developer. I am passionate about working with data as I love the challenges it affords. It is the field I want to teach new analysts who in turn will share this same passion in their new career.
Education was my first passion. I hold Bachelor’s and Master's Degrees in Education from the University of North Texas and being able to pursue both passions at this point in my career is very rewarding. Analytics skills are in high demand due to the amount of business data collected. Trained analysts are required to unleash the information within this data. This demand does not appear to be dampening and it is great to see students find success in the field.
SQL language skills are the basic building blocks to perform a variety of data analytics and data science tasks. The SQL language gives the analyst the power to convert data into information. To accomplish this high demand task, SQL is the language used for the creation of databases, the reading of data, and management of relational database systems.
Mastering SQL is a primary skill for the data analyst.
Reporting and analysis are the words you will often see on new job postings. Businesses continue to invest as it affords them visibility into the health of their business. It also allows them to mitigate risk when making go-forward decisions. As data volumes grow demand for skilled report authors and analysts will continue to grow as well.
Inferential statistics is a science that every analyst can benefit from mastering. The more confident an analysts is in their statistical skills the more confident they will be in presenting their findings to business leaders. Even though many say inferential statistics doesn't play a large role in analytics nor statistical learning a solid understanding allows for a deeper understanding of those disciplines.
Extraction, Transformation, and Loading of data (ETL) is the administrative process of moving data from transactional data stores to optimized environments for reporting. Although often viewed as a tactical administrative process, knowledge of proper data modeling increases the value of an ETL expert.
Successful reporting, analysis, and data science projects often credit the state the of the data model for the success. In turn, projects that often face challenges and delays find the issues primarily were related to a poor data model. The Kimball and Inmon warehouse strategies are quite mature and modern tools allow for easy implementation of good practices that align to either chosen methodology or hybrid thereof.
Advanced analysts often become interested in taking their skills to the predictive analytics space. Commonly referred to as "Data Science" the main idea of this field is to "discover the questions the business should be asking". This is an extension of the analyst's quest for finding answers from within the data available. Statistical and Machine learning can be accomplished with the aid of packages within the Python and R languages. R is the language of choice for many analysts as it was written from the bottom up for statistics and not general programming. Conversely, Python has been taking the lead in popularity due to advancements in specific libraries that make machine learning more accessible to programmers. It is best to learn one of these languages for data science and then learn the syntax for the other as needed.