The field of data analytics is vast and encompasses several careers ranging from software engineering to data science. Though the core goal of data analysis is to uncover underlying patterns and trends, there is a lot that goes into collecting the data, planning database schemas, building virtual infrastructures that will manage the flow of the data, predictive analytics and machine learning among other activities. For each of these roles, we have designated roles for them such as data architect, data engineer, data scientist and data product engineer just to name a few.
I have chosen the path of a data analyst. Data analysts explore, clean, analyze, visualize, and present information, providing valuable insights for the business. Structured Query Language is the tool of choice for accessing the database we are working on. Next, we leverage an object-oriented programming language like Python to clean and analyze data and rely on visualization tools, such as Power BI or Tableau, to present the findings. The essential technical skills that one need have as a data analyst are data visualization, data cleaning, MATLAB, R, Python, SQL, Machine Learning, Linear Algebra and Calculus and finally Microsoft Excel.
Key soft skills needed for one to be a good data analyst are Critical Thinking and Communication.
Data Visualization; This is a personโs ability to present data findings via graphics or other illustrations. The purpose of visualizing said data is to facilitate a better understanding of the insights gotten from analysis in an easy to understand manner. With data visualization, decision makers are able to identify patterns and understand complex ideas at a glance.
Data Cleaning; During this stage we perform several tasks to ensure the data is accurate, consistent and ready for analyses. This is a very crucial step because data that has not been properly cleaned will affect the integrity of any insights you generate from your analysis and impair the accuracy of your models. Some of the tasks performed during data cleaning are handling missing values, handling unnecessary duplicates, standardization, handling outliers and integration to name a few.
MATLAB, Python, R, SQL- These are some of the very essential languages that need to be mastered for one to be able to perform basic data tasks such as data mining, data cleaning and data visualization.
Machine Learning- This is a branch of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn from and make predictions or decisions based on data. A growing trend in the world today is automation of tasks. Boosting your skills as an analyst by having a general understanding of related tools and concepts of AI and in this case Machine Learning may give you an edge over competitors during your job search.
Linear Algebra and Calculus- When it comes to data analytics, having advanced mathematical skills is non-negotiable. Linear algebra has applications in machine and deep learning, where it supports vector, matrix, and tensor operations. Calculus is similarly used to build the objective/cost/loss functions that teach algorithms to achieve their objectives.
Microsoft Excel- MS Excel is essential for a data analyst to learn because it is a powerful and versatile tool widely used in the industry for data analysis, visualization, and reporting. Aside from its accessibility and familiarity, its Pivot table are one of the most useful dynamic features in analysis allowing an analyst to summarize, analyze, explore, and present data in a flexible way.
The key soft skills play the part of making one an all rounded analyst. Remember it is not enough to know how to generate insight and uncover underlying trends and patterns because at the end of the day, you need to be able to explain your findings to others.
The journey to becoming a data analyst begins with one step but as many who have walked that path will tell you, consistency is key. Going the extra mile to learn a new language or explore your data from a different perspective will set you apart from your peers.
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