It’s January 2022 and you’re ready for a fresh challenge.

You’ve heard all about the great resignation and you’re ready to pivot into a career in data.

You’ve enrolled in Udemy courses, EdX, YouTube Prep. Classes and you’ve come to the realisation; You don’t know what you don’t know, and the gap just keeps on growing.

In this article we’ll break down the bread and butter tools you’ll need to successfully take your pivot as a Data Professional in 2022.

Let’s get started.

Foundations

Let’s face it – there’s nothing worse than being unprepared for a job. But how do ensure you’re prepared? By building your foundation and base. The most important soft skills a budding data professional can seek to build is:

·         Basic Mathematical Proficiency

·         Structured Problem Solving

You’ll read guides that say you need to have calculus, geometry, linear algebra… you don’t. There’s a reason you don’t see that listed specifically on the job ad. As a Data Professional you’re going to be helping the business report and analyse the businesses Key Performance Indicators. By default, KPIs are high-level metrics that the business use to assess its performance. You won’t be the one creating these metrics, but you will be reporting on them which means you have to be comfortable with simple percentages, ratios and basic difference calculations (Month-on-Month Growth or Monthly Sales Variance). Provided you’re comfortable with these basic concepts, you’ll be able to carry out these calculations using a tool such as Excel or SQL that can help you get started with summarising and aggregating your data. We’ll cover these two skills later in the article.

Structured Problem Solving

Alongside basic mathematical aptitude, structured problem solving is one of the most critical skills you’ll need to seek to develop. It isn’t enough to be able to do basic mathematics; you have to be able to think critically to evaluate how you proceed with particular analyses and evaluate potential opportunities. Say for example, you’ve been asked to help Lyft examine its daily bike share data and you’ve been asked to create a high-level dashboard summarising your insights.

How would you start?

If you don’t have structure regarding how you approach the problem, you’re likely going to start haphazardly analysing the data in all directions and then trying to synthesize a story from it. We would instead recommend that you take a more structured approach using Issue Trees that demonstrates your logical thinking aptitudes. Issue Trees provide a high-level visual map that disaggregates your overall problem into bite-sized analyses that can be prioritised and solved, with an emphasis on tying this back to the initial problem statement. Using the basic mathematical aptitudes you’re proficient in, you’ll then be able to prioritise each branch and work out how to proceed with your analysis. This also helps avoid the paralysis-analysis phase of data analysis which is a common stumbling block for many would-be analysts who get overwhelmed by large datasets. 

Essentially, if you can master the fundamentals of structured thinking and basic mathematical proficiencies; you’ve got the basics ready for a career in data! Now let’s look at the core technical skills you’ll need to get noticed by recruiters and successfully pivot your career.

Technical Skills

Depending on which data professional route you take, you’ll need to become proficient with different tools and capabilities. There are four (4) main data analyst paths you can take where we’ve listed the job paths and their underlying technologies below:

Data Analyst

Visual Analyst

Business Intelligence Analyst

Business Analyst

Depending on which of the four (4) pathways you seek to pursue, you’ll need to build different competencies. However, the good news is that all these competencies are largely linked and can be used interchangeably. Summarising this into three major areas, you’ll focus on the following:

·         Extract the Data

·         Cleanse the Data

·         Explore the Data (i.e. Visualise / Analyse )

·         Summarise the Insights for your Data

·         Present your Data Analysis

Extracting Data from a Data Warehouse

The most common entry-level skill required for a Data role is SQL, which stands for Structured Query Language. Whether you’re aiming to work for Microsoft, Atlassian, a small start-up or even government organisations, you’ll need the ability to extract data from a database / data warehouse. As companies continually to adapt and drive their digital transformation, more and more data is being stored in data warehouses that analysts are expected to extract and analyse. Previously this was provided in the form of data cubes analysts could access, but with the broad democratisation of databases, analysts can now easily access a database and extract data with the right SQL competencies. The most common data warehouses / databases are Snowflake, Big Query and Postgres.

The key SQL functions to take note of are:

·         Common Table Expressions

·         Window Functions

·         Aggregations

·         Correlated Queries (Sub Queries)

Now, does it matter which SQL you learn? No. Whether you learn Postgres SQL or T-SQL (SQL Server), they all follow SQL standards, which is the standardised way to access data from a relational database.

Cleaning and Preparing Data

No matter what organisation you’re part of; you’re going to have become comfortable with cleaning and preparing data. Many assume that because data is in a relational database, it will always be clean. This is a false assumption. Cleaning and preparing data might be as simple as creating a Common Table Expression that joins multiple views together into one table. Or it could be more technical where you’ll need to backfill missing data via interpolation or clean up messy string data. Organisations will generally use ETL tools to help clean the majority of the data, but it’s important that you’re equipped with the fundamentals of splitting apart a messy dataset and re-joining this together to clean, otherwise, non-usable data.

For those just getting started, Power Query is a great option which is simple to use, but powerful in terms of the cleansing capabilities that are offered. Overall, however, we’re summarised the below options you’ll need to consider

The key data cleansing / preparation competencies to be aware of are:

·         Power Query (Duplicate Removal | Text to Columns Delimiter Splits)

·         Pandas (Interpolation, .replace() and .split())

Another plus to have would be the capability to understand how to construct and build out regular expressions that enable you to split apart string data in a more efficient manner than making use of .replace())

Exploring Data

Lastly, visualisation of the data you’ve so carefully curated and cleansed is one of the critical parts of your role as a Data Professional where you’ll seek to create meaningful insights that highlight the hypotheses/issues you’ve previously outlined in your analysis.

The means to achieve this now have increased significantly from the olden day of Excel Graphics and PowerPoint. Newer technologies such as Tableau & Power BI are expectations where the majority of Fortune-500 Corporations use one or both of these visualisation technologies. Beyond this, Python has some great visualisation alternatives with Altair, Seaborn and Plotly gaining popularity due to the cleanliness of the visualisations created and the ability to create interactive visualisations.  

Putting it all together in a Technical Workflow

Now in this article, we’ve glossed over the different underlying technologies you’ll need to master to land an entry-level data analytics jobs, but can you expect to use each bit of the technology in isolation like how they teach in 99% of all bootcamps? Realistically, no. This is dependent on organisation size, but generally, companies will have a deployable technical stack which will involve data / business analysts becoming familiar with the technical platform(s) used and then using this in a technical workflow such as the one shown below.

If anything, you *will not* be expected to do any heavy data engineering, but you will be expected to perform the following capabilities:

·         Create your own Problem Solving / Framing methodologies for scoping the Issue

·         Create your own Views from a Data Warehouse (Basic ETL)

·         Demonstrate data cleansing competencies (Python | Power Query | Excel )

·         Demonstrate data viz. competencies (Python | Power BI | Tableau | Alteryx )

·         Demonstrate the ability to work with a data workflow (i.e. Data Model à Query / View Generation in a Data Warehouse à Connecting to said View in Tableau / Power BI for Exploratory and Report Generation

·         Presentation of respective insights to business stakeholders (Executive, Technical, Non Technical) 

So, where can learn all of this and more? Whether you’re looking to be a business analyst, visual specialist, business intelligence analyst or data analyst, Tracked has the analytics program for you. You’ll need to master the core fundamentals of problem solving augmented by technical workflows, so why not learn the best practices from Guides at Fortune-500 Corporations like Atlassian and Canva?  Digital is rapidly changing and with it, the analytical landscape needs more and more talented data professionals. The Tracked Accelerated Program doesn’t just emphasise the importance of the technical workflows but does away with learning individual technologies in isolation through it’s cloud-based learning platform that seamlessly joins these technologies together.

Tracked’s expert curriculum was founded on the premise of affordability and hireable outcomes,   developed with experts from Microsoft, Salesforce and Harvard to name but a few contributors who sought to create an end-to-end analytics pipeline reflective of industry.

The programs 450+ hour curriculum covers:

·         Data Modelling

·         Data Cleansing & Preparation

·         Analytical Foundations as a Data Analyst

·         Extract, Transform and Load as a Data Analyst (Data Warehousing)

·         Insight to Value as a Data Analyst

·         Recruitment Portfolio Project (Cloud Based Workflows)

·         Data Analytics Exam

Alongside mastery of the core technical workflows required for business analysts, visual specialists, business intelligence analysts and data analysts; you won’t learn alone.

Tracked has an emphasis on creating custom small study pods where you can network and learn from your peers in a small dedicated pod ( 5 – 10 people), as opposed to a faceless group filled with hundreds of individuals that you can’t connect with. Your Guide will meet you on a weekly basis to build up your workflow fundamentals whilst you balance the career competencies you’ll need to successfully transition across as a fully fledged data professional.

If you’re interested in a career in data, consider the Tracked Accelerated or Comprehensive Analytics Career Track today. They’ll get YOU on Track for an exciting career in data.