The first year as a Data scientist can make you feel confused, and unsettled, regardless of whether you have a data science degree or not. The work environment is fast-paced in today's competitive world and, at the same time stressful, and intimidating.

This article offers a few steps for aspiring data scientists to make their first year in the data scientist role easier and more enjoyable. The aim is to provide essential tips for a data scientist to ultimately thrive in the first year in Data scientist role.

Get a Right Mentor

Finding a mentor is one of the most essential tips for a data scientist that someone can offer to someone starting. A mentor is someone with more experience in the industry, ideally at least a couple of years more than you, who is friendly, supportive, and wants to help you improve.

Your mentor could be anyone, from a coworker to an old friend who graduated earlier than you to a family friend who has been in the field for a long time. Ideally, your mentor is someone who also works in your organization. Although your manager can serve as a mentor in many ways, managers often already have a lot on their stuff, so getting a coworker with a few more years of experience than you to mentor you might be your best bet.

Reach out to the right mentor who can share their projects/code with you to serve as a reference point. Ask your mentor to review your code and find mistakes you wouldn’t have noticed. Check in with you weekly, biweekly, or even monthly to make space for questions, and concerns, and provide feedback on your progress.

Also, who points you to specific libraries, models, or other tools that have made their job easier but aren’t extremely well known?

If you find someone who you think would be a good mentor for you, you can reach out by email or LinkedIn, letting them know you are looking for senior people in the field to connect with. If things go well, you can ask if they’d be interested in mentoring you.

Make Your Fundamentals Strong

Master the basics. It is important to focus on this step among the essential tips for a data scientist as a fresher in learning and practicing the fundamentals of data science and understanding how they apply to your job. Many new data scientists are excited to dive right into the “cool” stuff - deep learning, LSTMs, sentiment analysis, etc. They want to try complex models and fancy methods of hyperparameter tuning.

Data Exploration is an essential topic to consider among the essential tips for data scientist, who are into the fresher role. So, are you capable of generating appropriate visuals and charts to thoroughly explore the dataset at hand? Do you possess the knowledge to examine the statistical properties of a data frame, including how to detect outliers? Furthermore, can you draw meaningful insights from the data exploration process to assist you in building an effective model?

Data Cleaning:  After analyzing the data to spot potential weaknesses or areas of importance, the next step involves deciding whether to remove outliers and null values. Alternatively, should these values be interpolated, and if so, what is the most effective method for doing this? It's also crucial to identify and rectify any incorrect data. Moreover, the ability to efficiently manage columns with various data types is essential. This includes converting data between different formats, such as changing a datetime to a string and then back again, converting a datetime to an integer, or an integer to a float.

Data preprocessing. Do you know how to generate new features from raw data (for example — creating time series columns like “hour” from a timestamp)? Are you able to encode categorical features (eg one-hot encoding, ordinal encoding, cyclical encoding) to feed proper inputs into your model? Can you scale your numerical values using MinMax or Standard Scaler (and do you know when it is necessary to do so)?

After thoroughly analyzing your data and extracting valuable insights from your workflow, the next step is identifying which features to initially include. Beyond this point, can you effectively determine the most impactful features for your model, while also identifying and removing any that negatively affect its performance?

Selecting and Training a Simple Model: Understanding when and why to choose linear regression over a random forest model, or an XGBoost model over a random forest, is crucial. It's also important to know how much data is necessary for effective training. Moreover, one should be proficient in evaluating the model's performance through cross-validation and train/test splits.

Choose the Right Model to Train

Understand which models to train. In certain domains, classifier models are preferred, such as for tasks like spam detection or computer vision and image classification. Meanwhile, regressors are used for time series forecasting. Depending on the available data and domain, it may be preferable to use statistical models like ARIMA or tree-based models such as Random Forest and XGBoost for forecasting.

Understand Your Field

The industry you operate in dictates distinct approaches for:

- Gathering and preprocessing data. Expect data to arrive in diverse formats and structures. You might need to become proficient in converting SQL queries into Python, or in handling JSON files. Different data types can vary in their susceptibility to spikes and outliers. In some instances, it's beneficial to remove these outliers, but in other scenarios, keeping them could be crucial for future predictions.

When selecting features for models, you will likely need to gather data from multiple sources. Initially, you may only have training data containing a timestamp and value. Depending on the type of data, you'll need to determine the features necessary to train your model. These could include weather data, customer demographics, other product sales data, and more. Domain knowledge is essential for identifying features that could significantly impact your target variable.

Take Online Classes

Consider taking online courses. Many companies are willing to cover the costs for you to take online courses and earn certifications. These courses can help you refresh your skills from your school days and also explore new areas of data science and machine learning that may not have been covered in your degree program. Data science degrees have limitations, and there may be gaps in your knowledge that need to be filled. After landing your first job, it's crucial to continue learning and address any knowledge gaps. Ask your manager if the company offers this benefit and how you can make use of it.

Seek Help

The chances are that someone else has already solved the problem you are facing. While you can find great information online, nothing beats having someone sit down with you and work through the problem. When it's a coworker, you have the added advantage of someone who knows the domain, business case, database, and data format.

Essential Tips for Data Science Students

If you're a student aiming to forge a future in data science, the above essential tips are tailored for you too.

In conclusion, as a fresher data scientist, you may not be perfect at contributing to applications in your organization. There are many challenges you might face in this role, so it is important to learn and implement data science tips or essential tips for data scientist to make progress.