Career Transition from Software Developer to Data Scientist can be tough, even scary! Not because you need to learn math and stats but because you also need to battle out the common mistakes people often make while progressing in their data science career. Avoid them, and you are all set for a successful career change from software developer to a data scientist. You would have already come across many career inspiring quotes and phrases encircling failure during your data science career transition: “You always pass failure on your way to success.”, “Fail Fast”, “Failure is a detour; not a dead-end street.” , “Never be afraid to fail”, “Mistakes help you grow”. However, the idea of mistaking your way to the top of the data science industry is somewhat rickety. Every Software developer will have their share of mistakes while progressing in their data science career but why not learn from the experience of other data scientists and avoid the costliest mistakes of all time in your data science career transition phase?
3 Common Mistakes to Avoid in Your Data Science Career Transition
That’s what we did: We spoke to Dr Kalpit Desai, Founder and Chief Data Scientist, Datakalp who helped us identify the mistakes to avoid when trying to make a career transition from Software Developer to a data scientist job role. Watch this video to know about some of the most notable mistakes to avoid while navigating to a red hot job market like data science
- Having a Code First Mindset and Not Data First
As a software developer one is bound to have the “Code-First” mindset. However, to succeed as a data scientist, change your mindset to “Data-First”. When working with a machine learning model, most of the actual cleansing and transformation happens at the data level. As an amateur data scientist, you need to emphasise and focus more on data than on the code because it is the data that will shape up the learning model perfectly at last. A model will be only as good as the data leveraged to train it. Having a highly efficient code(algorithm) is of no good use if you have poor quality data. Here’s how you can avoid this mistake –
a. Organise Data
Just like the way as a developer you organize code through version control, audit processes, and code reviews the same must be applied to the data assets you are working with. Data should be loaded into a raw database, version controlled, and the data assets should demonstrate compliance auditing.
b. Handle the Data Diversity
For example, Amazon’s facial recognition system had a tough time identifying female faces and people with darker skin as it was found to be biased on gender and race. What was the reason behind this ? The training set used to teach the system lacked data diversity. For a predictive model to make meaningful predictions and produce an accurate outcome for the machine learning jobs, the data fed into the model must be cleaned and transformed to handle diverse types of data to yield valuable insights. Feeding biased data to a model leads to dangerous outcomes sometimes. Lack of diversity in data can make many data science projects limited in their ability confining them to see all facets of the business problem. One path forward is to adjust your training data and make it better by including diverse case types to avoid bias.
c. Build a Reproducible Machine Learning Pipeline
In software development a CI/CD (Continuous Integration and Continuous Delivery) pipeline helps in the reproducibility of the outcomes by initiating code builds and running automated tests. Similarly, reproducibility of modelling is an integral part of a data scientist’s job to ensure there are no surprises and the results are matching every time a model is reproduced. As a data scientist if you cannot replicate the results on reproducing a model it can lead to financial costs and loss of time. To ensure full reproducibility of a model, follow these guidelines –
- Always save a snapshot of the data when training a model. This might not be feasible in case the dataset is large. In such cases, a practical solution would be to design and create data sources with well-defined and accurate timestamp columns.
- Any modifications or updates to how a feature is generated during model reproducibility should be version-controlled and tracked.
- Maintain a record of how a model is being trained i.e. an order of the features, feature transformations, hyperparameters used for training the model, and other details if any. For instance, if you are working with an ensemble model, ensure that you save the structure of the ensemble to avoid any challenges with model reproducibility.
- Software environment plays a vital role in full model resproducibility. Save the versions of each software package used for training the model or make use of containers for full model reproducibility.
2) Working on Kaggle Projects translates to Real-Time Data Science Industry experience.
Having Kaggle projects or hackathons listed in your data science project portfolio serve as a major safety net for data scientist jobs and are an excellent learning opportunity. These data science projects speak about your capability in solving data science problems practically and not hypothetically. However, these projects do not map to real-time industry experience as most of the steps involved in a data science project are missing. A typical data science pipeline includes the following stages –
- Translating a business problem to a data science problem
- Identifying the Data Collection Strategy
- Wrapping your head around data (Data Exploration, Data Preparation, Data Pre-Processing)
- Model development
- Model deployment in production
- Monitoring the feedback from end-users to tweak the model and incorporate the feedback.
In hackathons and Kaggle competitions the dataset is usually provided by the host organizations that are considerably well-structured and clean. These projects do not test your skills on data collection, data cleaning, and other data preparation techniques. Many amateur data scientists skim over and build negligence towards the concepts of data collection, data exploration, and data preparation forgetting the fact that making data usable for model development is paramount to emerge out as a successful enterprise data scientist. In real-time industry projects, a data scientist is required to do the data collection and preparation tasks which are complex and time-consuming. Moreover , most of the data science hackathons and competitions only test your skills in the model development stage (iv) while in the industry each stage of the data science pipeline is equally important and any analysis on inaccurate or inappropriate data is of no use.
3) Learning only one Programming Language
Most of the developers are in a comfort zone of working with a specific programming language but that might not be the ideal case when solving data science problems. Each programming language is brilliant at solving specific kinds of data science problems because of the unique features and packages each language offers. As a top data scientist, one must have a basic all-round knowledge of popular data science programming languages like Python, R, SAS, Java. However, having mastery over at least one programming language is necessary to emerge out as a champion. Master one programming language end-to-end and then incorporate another programming language into your skillset to learn more. Learning more than one programming language adds on to the versatility and competence as an enterprise data scientist.
These are some of the common mistakes that software developers often make during their career progression in data science and can be avoided easily with practice. Remember these common mistakes that other data scientists made to reduce your time to land a data scientist job. We hope avoiding these pitfalls in your data science career transition will help you create a positive impact in your new data scientist job role. With consistent learning, hard work, and a couple of good mentors you can make your data science career transition smooth. Put in some effort, time, and have passion, a few years down the line a software developer might come to you and ask: “I’d like to make a data science career transition, any advice.”
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