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Tips to become a Data Science Professional :


The Data Science jobs require domain specific skills and knowledge to perform the task. The skill sets vary from industry to industry, ranging from healthcare, banking, Ecommerce, insurance, logistics etc , The jobs in data science that are likely to boom by the year 2025 are data analyst, data scientist, data engineers, machine learning specialists, deep learning specialists, and AI engineers. A blend of both technical and non-technical skills are required to make your career path a smooth going affair. Here are a some tips that can help you out in your career journey.
1 Understanding the basics: You need to know the basics of core subjects in mathematics, statistics and basic programming. With this basic foundation grows your building ie., Data Science.
2. Cleaning and Manipulation of data: Cleansing, preprocessing and manipulate data (ie., remove redundant and irrelevant data), before doing analysis.
3. Develop your skills : on BigData, Programming languages such as Python or R, libraries, tools, Data Visualization Techniques, statistical model to make inference.
4. Build a Strong Portfolio : to showcase your skills to potential employers, which reflects on your exposure to the projects you have done.
5. Networking with seniors and other stake holders: Connect with other data scientists, seniors, managers. professionals from other departments, and other stakeholders, who are people with shared goals and interest, and makes you stay updated and connected with latest trends.
6. Develop your Analytical Skills and Critical Thinking : You can develop this skills by Testing a few Hypotheses and making some challenging Assumptions to it.
7. Develop your communication skills (both verbal and written): Communicate findings clearly to stakeholders and other non-technical persons.
8. Ensure Data Privacy : As you are dealing with sensitive data, you will need to ensure privacy of data.



Data Science Strategies:
The Data Science field is constantly evolving day by day with new technologies emerging on a regular basis. With that comes a greater scope for optimization. There are few strategies to deal with this trend.
1. Problem Definition: Define your problem clearly, understand the goals and objectives and understanding the problem etc.,
2. Analyze the data : After Cleaning and Preprocessing of data, data is transformed and formatted to make suitable analysis to draw insight. In the case of large and complex data sets of varied forms, to discover trends and patterns and draw insights from data, machine learning algorithms are used. You can use the various Python library for that like Keras for developing neural networks in machine learning models, PyTorch for NLP and computer vision for ML model. Matplotlib and Seaborn for data visualization, Theano for numerical computations in machine learning, TensorFlow is used to visualize machine learning models on desktop and mobile and many other libraries.
3. Exploring the data : Data is aggregated with Data Visualizations techniques, correlation analysis etc to understand the characteristics of data.
4. Choose a model : Based on the exploration of data, you can select an appropriate statistical model like regression, classification, clustering etc to understand the pattern.
5. Test the model : Based on the above model, you need to test it on the various subsets of data, to evaluate the performance of each subset, and ascertain the best among them.
6. Optimizing the model: if the model performance is unsatisfactory, you should explore ways to optimize it further.
7. Communicate your findings to the stakeholder : if the model is performing satisfactorily, you should communicate the findings to the stakeholders.
8. Collaborate with others : Collaborating with others can give you new insight and perspective on the problem.
9. Keep yourself updated with the latest trends : You should stay relevant by catching up with the latest trends and technologies.


Conclusion:


The above tips and strategies can help you navigate the job markets and boost your motivation and interest in breaking into the Data Science career. For that, honing the right skills set is important along with developing skills like business acumen and communication skills to work your way competently. Choosing for a career in data science today would mean treading a promising career path. I hope you will find these tips and strategies helpful. Contact edujournal (www.edujournal.com) for further queries.