5 CAREER SMASHING BLUNDERS: EVERY NEW DATA SCIENTIST SHOULD AVOID

Why do data scientists fail?

Started your first data science job? Congratulations and wish you speedy career growth. But do you know a few simple mistakes that you are making these days that may restrain your upcoming career growth as a?data scientist?

It’s possible that you are not aware of the fact but are committing these mistakes.

This blog will help you realise those five killer mistakes that every new data scientist does at the initial stage of their career and unknowingly make their future growth questionable.

Read this blog very carefully to avoid those pitfalls and ensure fluency in your data science career growth.

1.Assuming Algorithm complexity determine your expectation level

1. Complexity is the measure of expectation. I don’t know why the human mind gets stuck with this misconception. I have seen so many times that people appreciate a professional by saying ‘he has done a complex job.’ But such a thought is so insane.
2. Breaking a complex task indeed needs an extreme level of expectation, but making a simple task complex does not prove your expert level. Rather, it indicates your conceptual lack in the particular subject. Which is not a good sign towards data scientist career growth.
3. The same is applicable for machine learning algorithms. Maybe you will not confess and now about 85% of data science new bees are over fascinated about the?ML algorithms. They work with the target of designing such algorithms that are

• Trendy
• Adequately longer
• Complex enough to understand at the first or second site.

1. As the aspects of deep learning are in high demand, new data scientists assume the use of?deep learning algorithms?in their projects will enrich both their task and knowledge credibility.
2. However, the unnecessary elongation of the algorithm ensures nothing but the possibilities of error.

Key to Avoid such mistakes:

• Be smart enough to solve the problem with the least possible algorithm complexity.
• Work to evaluate the best-fit but shortest in length algorithm.
• Start thinking from the simplest algorithm like linear regression and gradually dive into the complex one, only if the simpler one is not working for your project.
• Don’t work to use a maximum possible number of algorithms, rather set a goal of keeping the number as much less possible.

2.Underestimating the domain knowledge and overpraising the algorithms

1. What was your key learning goal while you joined your data science certification course?
2. The majority of rising data scientists will answer, ‘learning the concepts of the maximum possible number of algorithms.’ Did you too complete your data science course with the same learning goal!
3. Well. In that case, you, too, make the biggest mistake. And obviously, after getting your first job, until now for every project kept overpraising the algorithmic aspects over the domain-specific situational aspects.

Are you getting confused? Let me explain a bit.

1. Data science is indeed based on statistical algorithms, but that doesn’t indicate that you need to learn all the algorithms. Instead, what you need to learn is to know the basic concepts about the same but need to earn proficiency about its scenario-based applications.
2. Note the term ‘scenario-based application.’ This term signifies the generation of an efficient Ml model that can solve the business problem.
3. Now, the competency of the?Machine Learning model?does not lie in the fanciness of used algorithms. Rather, the competency depends on how effectively you have applied your domain knowledge.
4. No matter how smart your designed algorithm is, it is not worth it until it fits your domain scenario and solution measures.

Key to Avoid such mistakes:

• Give priority to the application of your domain expertise and understand the problem for that aspect.
• Stop suffocating your identified data set with random data
• Focus more on data exploration based on your domain expectation and standard.
• Include the aspects of exploratory research while analysing the data.

3.Being overconfident about soft skills

1. I am entering the world of data science, so my skill achievement goal must revolve around data collection,?data mining, programming, statistical proficiency, and algorithmic knowledge.
2. This is a very common myth that the majority of new data scientists believe. If you do the same, then you, too, are committing the evilest mistake of your data science career.
3. Just analyse your daily tasks within your current job profile. The most common responsibilities include,

• Collect data from different sources and filter them based on their applicability, reliability, and usability concerning the identified business problem.
• Identification of the business problems experienced by different departments of your organisation.
• Explaining and convincing your peer groups with your analytical outputs and generated insights.

Now each of the above responsibilities needs to assess your proficiency in the following soft skills.

• Communication (for data collection from various department, understanding the exact problem they are facing)
• Critical thinking (for data reliability and applicability identification)
• Convincing skill (to make others confident about your analytical outcomes and insights)

1. As a data scientist, your key goal is to design and deploy such an ML model that provides sustainable and ultimate solutions to the communicated/ targeted business problem throughout your organisation. Lack of communication can enhance critical thinking ability

• Degrade the re-usability of your solution model.
• Ineffective algorithmic solutions.
• Misinterpretation of data.

Key to Avoid such mistakes:

• Increase internal and external communication
• Question departmental experts about the targeted problem and clear all the doubts before generating the hypothesis.
• Critically analyse the viability of collected data based on your domain knowledge and specific situations

4.Considering data investigation and visualisation as a quick task

1. What are the most common mistakes in data science when collecting the data?
2. Generating and deploying the ML model always remains the key target of a new data scientist. Completing and deploying machine learning models indicates that the project is about to finish. Hence reaching these steps become the foremost target of every data scientist. As a consequence of such a thought, the majority of data scientists ignore the significance of data investigations and the after-analysis data visualisation.
3. Most new data scientists invest the minimum possible time of data research and post-modelling visual outcome presentation.
4. But this is one of the most career threading mistakes. It makes your generated insight as well as the machine learning model questionable in terms of

• Efficacy
• Capability
• Future reusability.

1. Even the business decision taken based on such modelling may lead to business running error.
2. Not only that. Data visualisation also plays a vital role in business decision making. Visualised outcomes are the elements that help others (audience/ clients) to understand the significance of your data analysis, based on which respective business decisions are taken. The wrong visualisation can cause wrong interpretation as well as wrong business decisions.

Key to Avoid such mistakes:

• Research more on the collected data
• Use exploratory data analysis techniques.
• To ensure the reliability of considered data, start your data analysis planning with questions like ‘Why?’, ‘What?’ and ‘How’ concerning the identified business problems. Then, start exploring data to find the target solutions.
• Appy ‘check, recheck and cross-check’ strategy to identify the usable data from collected datasets in terms of reliability, ethics measures.
• Identify and rectify any data gap or data misplacement before heading to the modelling stage.
• Choose the right data visualisation tools and features (suppose our outcomes are best presentable with a simple bar graph, but to make it trendy unnecessarily, you choose a waterfall chart).
• While choosing data visualisation tools or features focus on those options that offer the maximum scopes of variable insight generation concerning a targeted business problem.

5.Avoiding the usage of probabilities

1. What are the common data mining and data analysis mistakes?
2. The answer is overlooking the importance of probabilities.
3. In the above point, I have mentioned that you should start data exploration with targeted solutions. But that doesn’t mean you can ignore the associated possibilities.
4. Yes. Several statistical pieces of evidence prove that in our real world, every problem has more than one solution. To make your data-driven solution best fit the identified business scenario, you must consider all of those probable solutions.
5. Is it becoming a bit complex to understand? Well. Let me give a simple example.
6. Suppose one of your company products is not getting the expected market response. As a solution, you have decided to decrease its price by 5% permanently. Alternatively, there also lies a possibility of fostering sales growth by dropping the price by 10% for a very limited period of time. Once the product wins, the customer’s reliability and e, then getting back to the initial price will not affect the buyer’s interests. Instead, a permanent price drop of 5% may lead to loss.
7. Hence clear that ignoring the probabilities associates high chances of landing on erroneous business decisions.

Key to Avoid such mistakes:

• Explore the maximum possible, probable answer to your hypothesis.
• Use scenario planning strategy and probability statistics for data modelling and identification of targeted populations.
• In the case of population identification, never population with lesser probabilities because considering only the population with the highest popularity may lead to serious business decisions.

Where to learn to become a successful data scientist?

You are now well aware of the common mistakes that most data scientists make at the beginning of their career. But, unfortunately, not only these, but they’re also eating so many other silly mistakes that may slow your data science career growth.

Suppose you plan for a successful data science career transition and don’t want to start your data science career without making silly mistakes. In that case, you can join the Data Science and AI Certification courses of Learnbay.

Each course module of Learnbay is designed for working professionals that make you adequately eligible to address your domain knowledge throughout your data science job responsibilities. These courses train you about data science concepts, tools, and technology and make you aware of common mistakes to be avoided.

To enrol for our data science courses, book a telephonic profile review?here.