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In my experience with Data Science aspirants over the last few months, I have realised that there is no one specific method or roadmap to become a data scientist. Data Science is a vast field. I have seen a dynamic range of people who came from different background, with different experience and different skill set become successful data scientist .It is vital for career aspirants to be acquainted with in-demand technologies in the realm of data science such as R, SAS & Python. And being able to implement concepts pertaining to data exploration, regression models, hypothesis testing, etc.I suggest you look into these 7 things and see how much of each you know - and the practice the ones that seem unfamiliar to you.Basic Programming Languages: You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL.Statistics: You should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators and confidence intervals. Statistics is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments.Machine Learning: You should be able to explain K-nearest neighbors, random forests, and ensemble methods. These techniques typically are implemented in R or Python. These algorithms show to employers that you have exposure to how data science can be used in more practical manners.Data Wrangling: You should be able to clean up data. This basically means understanding that "California" and "CA" are the same thing - a negative number cannot exist in a dataset that describes the population. It is all about identifying corrupt (or impure) data and correcting/deleting them.Data Visualization: Data scientist is useless on his or her own. They need to communicate their findings to Product Managers in order to make sure those data are manifesting into real applications. Thus, familiarity with data visualization tools like g-plot is very important (so you can SHOW data, not just talk about them)Software Engineering: You should know algorithms and data structures, as they are often necessary in creating efficient algorithms for machine learning. Know the use cases and run time of these data structures: Queues, Arrays, Lists, Stacks, Trees, etc.Product Management: This one is definitely debatable, but those who understand the product are the ones who will know what metrics are the most important. There are tons of numbers one can A/B test, so product-oriented data scientist will pick the right metrics to experiment with. Know what these terms mean: Usability Testing, Wireframing, Retention and Conversion Rates, Traffic Analysis, Customer Feedback, Internal Logs, A/B Testing.In each field, I mentioned few buzzwords you should know about. As there are hundreds of resources online but its highly unlikely that they are personalized to understand the individual queries faced by career aspirants in the realm of Data Science.I would recommend visiting Board Infinity as we provide rigorous hybrid training programs for students and career aspirants. Learn in-demand skills & tools pertaining to the field of data science such as R, SAS, Python and implement concepts such as data exploration, regression models, hypothesis testing etc.• Curated by 200+ Coaches• 1:1 personalized mentoring by Industry Experts• Placement AssistanceIf you have further queries pertaining to Data Science Career PathPlease feel free to refer the following blog posts:What are the most important things to have on a Data Science resume?Difference between Machine Learning,AI, Data Science & Deep LearningWhy do companies hire big data analysts?Wish you the best with your future endeavors & Welcome to Data Science!

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Hey there! I am really proud of the UX design process we have in Ideamotive and I will be superglad to share some insights :) Let’s roll!Step 1: DefiningTypically, we start by brainstorming with the client to gather all the main UX functionalities and envision the context of the product. We will also try to extract the key value we want to offer to the user. In order to do that, we need to answer some questions:· What is the purpose of using the product?· Who is likely to use it, how often and in what context?· What is the added value?· What is the main content and functionality of the application?· What are the commercial and functional goals of the project?The “Defining” step often concludes with concept sketching – even on a piece of paper – to visualize the idea.Step 2: ResearchAfter we have arrived at a clear definition of the product, we will need to carry out user and market research. This stage has two main purposes. The first is to better understand users’ needs. The second is to become aware of the state of competition and market in general.Basic action steps during this stage may include:· Individual, in-depth interviews with users;· Competition research and looking out for similar, existing products;· Finding opportunities for product implementation.Step 3: AnalysisAfter having conducted the initial research, we are beginning to understand the context in which we will be creating the product. Now it is time to organize the data we collected and lay a solid foundation for the design process.Analysing all the information we have allows us to identify the elements of UX design that are still missing and plan our next actions.· We identify the opportunities for innovation and improvement of the UX of product;· We define personas as references for key audience groups;· We name users’ feelings and behaviours that are mostly likely to lead to actions;· We identify priority behaviours and, based on them, priority objectives;· We plan a roadmap that will lead us to reach those objectives.In the end of the Analysis step, we have a wide understanding of what will make for a good UX in the product. We also have the complete roadmap to create it.Step 4: Design – low to medium fidelityThis is probably the most rewarding part of the UX design process. Finally, all the concepts we have been coming up with are taking shape in a visual form! Most of the times, this “low to medium” design stage involves four sub-steps.Note that these sub-steps might not always be linear. For example, we might go back from “wireframing” to “sketching” if we discover that something needs improvement on a very basic level.This step contain of 4 substeps:1. Sketching helps to quickly come up with visual representations of the most basic functions. This step often results in a variety of approaches to a single element (or function), and allows to pick the most optimal variant in the end.2. Creating user flows and maps allows to determine what actions and content are available at specific stages of the application flow. This is when we establish connections between certain elements of user experience, discover possible interactions and tweaks to them. This sub-step allows for significant optimization of the product’s usability.3. Wireframing is when we set up the information structure for key functionalities, their placement and scale. Wireframes created at this stage are filled with exemplary content, and their purpose is to make sure that every function of the product is represented in a proper way.4. Prototyping is the final step which lets us verify the process, check screen-to-screen interactions, and tell whether the feel of the application flow seems right. After receiving feedback on the prototype, we often go back to the “wireframing” stage to make necessary adjustments.Step 5: Design – high fidelityWhen we are happy with the medium-fidelity prototype, it is time to design the final experience for the users. The last stage of our UX design process results in giving the product the definitive feel and look, which build up the experience of the end-user.Firstly, we need to establish the design system (DS). This involves all the colours, fonts and other visual elements assigned to the product. Having a set of DS files is a backbone for every UX project, because it allows for easy adjustments to the look of the product.Secondly, we finalise the visual design. This means replacing basic shapes of the wireframes with actual assets from the DS and with other external components – such as custom photographs and data. This step results in the final look of the product in the current milestone, which can still be altered after feedback.