As a junior data scientist, you’re not expected to possess all the advanced, high-level skills of an experienced professional. After all, everyone has to start somewhere. And data science is one of the most challenging disciplines to build up all the skills required.

Nonetheless, a junior data scientist should have a solid set of core competencies. To do the job, whether in a support role or as an analyst for a small business, you should be able to do several key tasks without excessive effort and time.

In particular, a junior data scientist should have the skills required to competently: build datasets, clean and manipulate data, make data accessible to users, perform advanced analytics, do modeling, present data statistics visually, and align data handling processes with the needs of an organization.

In terms of developing the skills required for a junior data scientist, what should be the priorities? Let's go through the core competencies you should have to start working in data science.

1. Programming

Programming is a crucial skill to have for budding junior data scientists. Python, R and SAS are the most popular programming languages for data scientists. Structured Query Language (SQL) is also helpful for data queries and managing relational databases.

A routine part of junior data scientists' job description is to use programming to organize large and often unstructured swathes of data to make analysis possible. You can learn a programming language by pursuing a degree or signing up for an online short course. Once learned, programming is a skill that will help you in many fields, even beyond data science.

2. Statistical Methods

Statistics is a fundamental element of data science. As data engineer Josh Wills once put it, “A data scientist is a person who is better at statistics than any programmer and better at programming than any statistician.”

Any good course that prepares you to be an applied data scientist will focus attention at some point on statistical methods. Subjects such as Statistical Methods for Data Science and Statistical Inference for Data Scientists are commonly found in four-subject Graduate Certificate in Data Science courses.

Statistical methods that data scientists need to know include linear regression, logistic regression and discriminant analysis, bootstrapping and cross-validation, partial least squares, step functions, and clustering.

3. Machine learning

For data scientists, machine learning is a technique you must understand. Machine learning is used to create predictive models. For example, you can use a machine-learning algorithm to show data from the previous month and estimate how many users you will have next month. Predicting outcomes is an essential part of business analytics, especially in marketing.

You can start with simple linear models and logistic regression and then move on to more advanced models like Random Forest, XGBoost and CatBoost. It is good to know the rules of these algorithms (it only takes 2-3 lines), but the most important thing is to understand how they work. This makes it easier to tune hyperparameters and, ultimately, produce a model with low error rates.

The best way to learn machine learning is to practice explaining problems. You can join events such as HackLive, a community leadership community hackathon, where you can learn from the experts while solving the problems and contributing to participating in a hackathon.

4. Data Visualization

Presenting your results graphically is one of the most fun things about data science. Visualization is more of an art than a fixed scene. So, there is no "one size fits all" method here. Instead, visual experts know how to create interesting stories.

To get started, you need to get familiar with common charts like histograms and bar charts and move on to other charts like waterfall charts, temperature charts, and more. These presentations are beneficial when analyzing or showing research data. However, univariate and bivariate analysis is easier to understand using graphic art.

Tableau is a popular tool of the trade, used by many, though not all, data science teams. The visual analytics platform provides a smooth interface with drag and drop functionality.

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5. Deep Learning

Deep learning, a machine learning method, simulates some of the ways people gain knowledge. You should be familiar with how this works, even as a junior data scientist. To be successful in this field, you should be proficient in programming (preferably Python) and understand linear algebra and math.

You can start by creating simple models and then move on to other models such as CNNs, RNNs, etc. Libraries like TensorFlow, Keras, and PyTorch are must-haves if you're looking for in-depth study.

6. Software Engineering

Junior data scientists with software expertise are in higher demand. They are more self-sufficient in terms of programming skills, making them significantly better equipped to work with data independently.

Some data scientists develop software themselves that can be updated with new data. Others use programming to generate visualizations that can be used by business teams to make decisions. A data scientist with software engineering skills is not constrained by existing data science tools or the capacity of the data analytics team; instead, they can write their own programs.

Becoming a good software engineer without earning a bachelor's, master's, or associate's degree would generally be challenging because of the complexity of the subject and breadth of skills required. Explore industry-aligned courses and programs if you're interested in learning software engineering foundations or adding such skills to start your data science career.

7. Big Data

Making effective use of Big Data is a key facet of data science. Data volumes are often large and generating insights from datasets effectively presents logistical challenges. Typical big-data tasks for a junior data scientist include predictive modeling, designing segmentation algorithms, recommender systems, and A/B testing frameworks.

8. Data Manipulation and Analysis

Another important skill for a junior data scientist is data manipulation, which involves cleaning raw data, removing outliers, modifying null values, and converting the data into a more usable format. Junior data scientists might get faster conclusions by manipulating data elegantly, especially when dealing with enormous amounts of data.

Data manipulation and analysis can take a long time, but they will help you make better data-driven judgments in the end. Missing value restoration, outlier correction, adjusting data types, resizing, and transformation are some of the common data manipulation and analysis techniques used.

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