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How can you demonstrate your data analytics skills during an assessment test?

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Data analytics is a crucial skill for data scientists, but how can you showcase it during an assessment test? Whether you are applying for a job, a course, or a project, you may be asked to complete a data analysis task to demonstrate your abilities and knowledge. In this article, we will share some tips on how to prepare for and ace your data analytics assessment test.

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Sudip Bhattacharya
Ankita Chelani

2x LinkedIn Top Voice | BI and Data Analytics | Trainer and Educator | Microsoft certified (Power BI and Fabric) |…

1 Understand the requirements

Before beginning any data analysis task, it is essential to understand the test, the expected output, and the allotted time. Carefully read the instructions and don't hesitate to ask for clarification if anything is not clear. Exploratory data analysis involves summarizing the main features and insights of a given data set. Data cleaning and preprocessing requires applying techniques to make the raw or messy data ready for further analysis, such as dealing with missing values, outliers, duplicates, or errors. Data modeling and inference involves using statistical or machine learning methods to answer a specific question or hypothesis, such as regression, classification, clustering, or testing. Lastly, data visualization and communication entails creating charts, graphs, or dashboards to effectively communicate findings and recommendations to a target audience.

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Sudip Bhattacharya
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Practicing well, prepare and become ready to effectively demonstrate data analytical skills in an assessment test. - Identify key / primary data points and objectives of the assessment test - Define the outcomes in form of conclusions, actionable recommendations - Uncover insights through visualisation and co-relation analysis - Create clean and consistent data - Structured approach with depiction of patterns and trends from data - Demonstrate proficiency in the data analysis tools based on the requirements of the assessment test - Proof read, amend before submitting

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Jayanth MK

Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance Trainer | Instructor | Mentor | Data Science | Machine Learning | AI | NLP/CV |
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Success in a data analytics assessment hinges on understanding the task requirements and choosing the right tools. I prioritize a meticulous read-through of instructions, ensuring clarity and seeking clarification when needed. Tailoring the approach to each phase—whether exploration, cleaning, modeling, or visualization—is crucial. Selecting tools aligned with the task's complexity is key. Following a structured data analysis process and consistently reviewing and refining work leads to a polished final output. This approach, rooted in clarity and methodical execution, has proven effective in showcasing my data analytics skills in assessment tests.

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2 Choose the right tools

When it comes to data analysis tasks, the type and complexity of the task will determine which tools and software are most suitable. Excel is a popular spreadsheet program that enables basic calculations, formulas, functions, and charts. However, it may not be enough for more advanced or large-scale analysis. R is a programming language and environment that provides packages and libraries for data manipulation, analysis, modeling, and visualization; however, it requires more coding skills and experience than Excel. Python is another programming language and environment that has many modules and frameworks for data science; it is similar to R in terms of functionality and versatility but may have a simpler syntax. SQL is a query language that allows access to data stored in relational databases; it works well with structured and large data sets but may not be compatible with other types of data or tools. Tableau is a software application that specializes in data visualization and dashboard creation - it is user-friendly but may not offer as much customization as coding tools. Ultimately, the best tool for your data analysis task depends on your personal preference, skill level, and availability.

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Jayanth MK

Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance Trainer | Instructor | Mentor | Data Science | Machine Learning | AI | NLP/CV |
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Choosing the right tools for data analysis is a pivotal decision, and my approach is driven by task complexity and personal proficiency. Excel is versatile for basic tasks, but R and Python offer more advanced capabilities, each with its coding requirements. Having worked with both, I find Python's syntax more straightforward. SQL excels with structured databases, while Tableau, though user-friendly, may lack customization compared to coding tools. The key is aligning the tool with personal preference, skill level, and the specific demands of the analysis task. This tailored tool selection has proven crucial in optimizing efficiency and results in my data analysis endeavors.

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3 Follow the data analysis process

No matter the tool or task, it's important to follow a systematic and logical process when conducting data analysis. Generally, this process involves defining the problem, collecting the data, exploring it, cleaning it, analyzing it, visualizing it, and communicating it. Defining the problem requires identifying the main question or objective of the data analysis and narrowing it down to a specific scope. After gathering relevant data from various sources, such as files, databases, APIs, web scraping, surveys or experiments, you should explore the data to understand its structure, size, type, quality and distribution. Cleaning the data requires applying techniques to improve its quality and usability. Analyzing the data involves applying methods to answer your question or objective. Visualizing the data involves creating charts, graphs or dashboards to display your results and findings. Finally, communicating the data necessitates summarizing and interpreting your results and findings while providing insights, conclusions or recommendations based on your data analysis.

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Jayanth MK

Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance Trainer | Instructor | Mentor | Data Science | Machine Learning | AI | NLP/CV |
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Following a structured data analysis process is my compass in navigating the complexities of tasks. From defining the problem to communicating results, each step is a crucial building block. My experience underscores the significance of a meticulous exploration phase, where understanding data structure and quality sets the tone for effective analysis. Cleaning the data ensures it's not just accurate but also usable. Visualization, a personal favorite, brings insights to life. Communicating findings is an art, involving summarizing results and offering meaningful recommendations. This process-driven approach, regardless of the tool, has been pivotal in elevating the impact and coherence of my data analyses.

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4 Review and improve your work

Before submitting your data analysis task, take some time to review and refine it. Ensure accuracy by checking your calculations, formulas, functions, code, queries, and models for any mistakes or bugs. Make sure your work is comprehensive and covers all the relevant aspects of the problem. Also, ensure your analysis is relevant to the question or objective of the test and is communicated clearly and understandably. Additionally, strive for creativity by using innovative techniques or tools and providing novel insights or recommendations. By following these tips, you can demonstrate your data analytics skills during an assessment test and impress your evaluators with your work. Data analytics is a valuable and rewarding skill for data scientists which can be improved through practice and learning from different data sets and problems.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ankita Chelani

2x LinkedIn Top Voice | BI and Data Analytics | Trainer and Educator | Microsoft certified (Power BI and Fabric) | Certified Scrum Master
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You should be able to use your business acumen along with technical skills while preparing the assessment. You should also be able to choose the appropriate visual for the situation in hand to highlight the key insights. Also, in my experience, you should focus on not making things complicated in order to stand out. The end result should be unique but at the same time, should not be overwhelming for the end users.

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How can you demonstrate your data analytics skills during an assessment test?

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5

1 Understand the requirements

2 Choose the right tools

3 Follow the data analysis process

4 Review and improve your work

5 Here’s what else to consider

Data Science

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How can you demonstrate your data analytics skills during an assessment test?

1

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3

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5

1 Understand the requirements

2 Choose the right tools

3 Follow the data analysis process

4 Review and improve your work

5 Here’s what else to consider

Data Science

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