A large part of the professional future lies in data specialist profiles. Data Scientist, Data Analyst, Data Engineers, Data Viz… Some time ago we talked about the diversity of profiles around data (article available in spanish) that are being generated around these professional opportunities.

Perhaps you have considered entering this world but don’t know where to start. To help you, at Keepler we have an internal Data Path from which we have extracted part so that it can become a guide in your first steps into the world of data and become a Data Analyst, one of the professions that always appear in the Top 5 of the most demanded for the coming years. Who knows, maybe it will be the beginning of a professional change for you. 

SQL

SQL is a very specific language for managing and retrieving information from relational data management systems. It is a highly standardized language for managing relational databases and performing various operations on the data they contain. It is commonly used by developers who write data integration scripts and by data analysts who configure and execute analytical queries. 

 

Data Modeling

We understand Data Modeling as a process of designing and creating standardized data models. It is a very important phase underneath the structure on which the data is organized, allowing to illustrate the relationships between them. It is a somewhat slow process because it takes time to identify all possible relationships in a model, but properly documenting conceptual, logical and physical data makes it easier to identify errors and possible changes before entering into coding phases.

Coding in Python

Python is a programming language that aims at code readability. It is a simple language to read and write, as it is very similar to human language. Perhaps for this reason, it is very popular for the creation of web applications, data analysis and automation of operations. It is widely used in the field of data science thanks to its simplicity and the large number of libraries available and the huge community that supports it. 

  • [EN] Learn Python for free (ALL) It covers the fundamentals (data types, loops, lambda functions, and so on).

 

Data Analysis

Data analysis (or Data Analysis) is a series of tasks and techniques aimed at analyzing raw data to draw conclusions and the most relevant information from it, find trends and metrics that add value to decision making. It allows organizations to identify the most efficient ways to use huge amounts of information. 

We identified 4 types of analytics:

  • Descriptive: describes what happened in a given period of time.
  • Diagnostic: it tries to explain why certain things have happened and to establish a diagnosis.
  • Predictive: focuses on predicting events that may occur in the near future.
  • Prescriptive: recommends actions and foresees the impact they will have.  

 

 

Data VIZ 

Data Visualization is the technique that collects all the information and data and translates it into a visual representation that facilitates understanding and interpretation in the most accurate and effective way possible to draw conclusions, share ideas or make decisions.

 

BI Tools

BI (or Business Intelligence) are the strategies and tools that allow transforming simple data into information and knowledge that bring value to the organization and help decision making. Normally, it combines internal and external information of the organization from very different origins and sources. 

Business intelligence is becoming more and more important in companies due to the increasing volume of data and its complexity, which makes it necessary to process, analyze and present it in an understandable way for the different actors that consume it.

 

Power BI

Power BI is Microsoft’s data analysis service that allows you to generate interactive visualizations in an interface that is easy to consume and use by end users, allowing them to create reports and dashboards on their own. 

 

Quicksight

Amazon Quicksight is the AWS business intelligence service that connects data from a wide variety of sources to the cloud. It provides easy-to-understand information to everyone in the organization, through natural language queries, interactive dashboards or automatic pattern and outlier search powered by machine learning.

 

Tableau

Tableau is a visual analytics solution from Salesforce, helping people see and understand data. It allows you to easily explore and manage data and quickly discover and share information. 

  • [EN-SP] Free training videos (L1). To do your first steps with Tableau. Official free training videos.

 

Qlik 

Qlik is an enterprise analytics platform that turns raw data into informed action, a real-time analytics platform and end-to-end hybrid cloud data integration.

 

Microstrategy

Microstrategy is a BI and business analytics platform developed in hybrid cloud. It enables powerful functionalities such as data discovery, advanced analytics, visualizations, business intelligence, reporting and more. It also facilitates application customization and integration with third parties. 

 

Data Engineering

Data engineering designs, develops and maintains systems that process large volumes of data. The Data Engineer is responsible for building and maintaining the data structures and technology architectures required for the ingestion, processing and large-scale deployment of data-intensive applications. 

  • [EN] Data Engineering Nanodegree Udacity (L3). Well explained course with a wide variety of projects and exercises. The course is based on AWS services and it is assumed that you have a good level of SQL and some knowledge of Python. Note that it is a nanodegree and it has a high level from a DA perspective.

 

Cloud basics

The public cloud offers more and more job opportunities and its knowledge and operation is an additional value for data specialists. At Keepler we have put together a complete Cloud Learning Path that will help you get into the public cloud of AWS, Google Cloud and Azure and add that knowledge to your experience (available in spanish).

https://keepler.io/es/learning-cloud/

 

We are sure that if you are able to follow this path and acquire all the knowledge from these trainings and readings, you will have taken the first step to orient your professional career towards a market with great professional opportunities.

 

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Author

  • Data Analyst en Keepler. “Engineer passionate about data and BI. As a Data Analyst, the part of my job that I like the most is being able to find the best way to analyze and display information. In the projects I am part of, I consider essential to form a team based on trust and good communication between specialists in different areas.”