What should you know before starting a career in Data Analytics?
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Data analytics is a fast-growing and exciting field that can offer many opportunities for career advancement and personal growth. But before you dive into this domain, there are some things you should know to prepare yourself for the challenges and expectations of data analytics roles. In this article, we will cover six aspects of data analytics that you should be aware of before starting your journey.
Data analytics requires a combination of technical, analytical, and business skills. You need to be proficient in using various tools and platforms to collect, process, analyze, and visualize data. Some of the common tools include Excel, SQL, Python, R, Tableau, Power BI, and Google Analytics. You also need to have a strong foundation in statistics, mathematics, and logic to apply the appropriate methods and techniques to solve data problems. Additionally, you need to have a good understanding of the business context and objectives of your data projects, and be able to communicate your findings and recommendations effectively to various stakeholders.
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Gaurav Agrawal
Data Science & Analytics @Walmart | Ex- Analytics Manager at Swiggy
In my experience, analytics is about helping business make data driven decisions. Tools like SQL, Python, Excel , Power BI & Tableau is just a means to solve the problem. If one needs to excel in this career, then one should be a great problem solver, my ~7 years of career has taught me this.
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Athanassios Staveris-Polykalas
Bridging Innovation & Strategy
Entering data analytics necessitates a foundation in statistical mathematics and sharp analytical thinking. Proficiency in programming (Python or R), database management (SQL), and data visualization (Tableau or Power BI) is essential. While familiarity with machine learning is a plus, the ability to clearly communicate findings and understand business contexts is critical. Detail-oriented data cleaning skills, awareness of data ethics and privacy, and a commitment to continual learning are also key to success in this fast-evolving field.
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Ankita Singh
Helping organisations make informed decisions through insights derived from data
To understand and execute data analytics, one should be able to think logically and break the problem statement into small binary questions of 1 (true, yes or do) and 0 (false, no or don't do).
Data analytics involves working with different types and sources of data, depending on the industry, domain, and scope of your projects. You may encounter structured data, such as numerical or categorical data that can be easily stored and queried in databases or spreadsheets; unstructured data, such as text, images, audio, or video that require more complex processing and analysis; or semi-structured data, such as JSON or XML files that have some elements of both. You may also deal with data from various sources, such as internal systems, external platforms, web scraping, APIs, surveys, or social media. You need to be familiar with the characteristics, advantages, and limitations of each type and source of data, and how to access, integrate, and manage them.
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Benjamin Bennett Alexander
Before you start your career in data analytics, Here is what you need to: 1. You will have to add Python and SQL, to your skill set. 2. The tools required for your job will evolve over time, so be prepared to continuously be learning. 3. Do not downplay the power of soft skills. Soft skills will take you places that hard skills can only dream.
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Leandro Araque
I help IT leaders visualize 100% customer satisfaction, without complex integrations, late reporting and excessive use of resources with the IF. | Harvard CORe | LinkedIn Community Top Voice
I’ve found data engineering skills crucial for managing and orchestrating data flow from diverse sources. Understanding data provenance and quality is vital as it directly influences analysis reliability. Often, projects require blending structured and unstructured data, necessitating proficiency in data integration tools and knowledge of both NoSQL and traditional SQL databases.
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Partha Sarathi Kundu
Serverless Developer | AWS Certified | AWS Community Builder
Building effective communication skills to translate data insights into actionable recommendations is equally important, as it bridges the gap between data and decision-makers. In summary, a blend of technical expertise, practical experience, and effective communication is key to a successful career in Data Analytics.
Data analytics relies on the quality and integrity of the data that you use for your analysis. You need to be aware of the potential issues and challenges that may affect the accuracy, completeness, reliability, and validity of your data, such as missing values, outliers, duplicates, errors, biases, or inconsistencies. You need to apply the best practices and standards to ensure the data quality, such as data cleaning, validation, transformation, and documentation. You also need to be mindful of the ethical implications and responsibilities of using data, such as data privacy, security, consent, ownership, and compliance. You need to follow the relevant laws and regulations, as well as the ethical principles and codes of conduct, when collecting, storing, sharing, and analyzing data.
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📈 Camilo Cely 💻
★ Technology-Oriented Business Executive | Business Intelligence | Data Analytics | Business Outcomes | Leadership | People Management | Lifelong Learner | Solutions-Oriented | AI | Business Analytics
Data quality is a critical aspect of the analytics process. For any analysis to be valuable, you must have the 'right data.' This involves ensuring that the information used as the foundation is reliable, accurately reflects what is being analyzed, and can be used for making business decisions. Otherwise, no matter how accurate the analysis model is, the results simply can't be trusted. It's a classic case of 'Garbage In, garbage out.' Having data quality is the most important part of the process. My recommendation is to be prepared to spend a significant portion of your time working with data to ensure it's ready as a high-quality input. This includes data cleaning, data transformation, scaling, standardization, and more
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Leandro Araque
I help IT leaders visualize 100% customer satisfaction, without complex integrations, late reporting and excessive use of resources with the IF. | Harvard CORe | LinkedIn Community Top Voice
Data quality is paramount. Data cleansing and preprocessing techniques ensure analysis integrity. Ethics in data analytics is equally critical; respecting privacy and consent is not just a legal obligation but a moral imperative. Embracing standards like GDPR and data ethics principles helps build trust and ensures the sustainability of data initiatives.
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Chiwama Michael Mwela
Data Analytics Senior Specialist @ Zambia National Commercial Bank (Zanaco) PLC | Data Mining, Data Analytics
Data ethics are very important. As a data professional, you will be exposed to very sensitive data. For instance, if you are a bank data scientist, you will have sight of very sensitive customer financial information. You will know, how much they earn and owe, where they spend their money, how they spend etc. You need to maintain highest levels of ethics to ensure confidentiality of the customer information. This info in the wrong hands may have dire consequences.
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Data analytics is not a linear or straightforward process, but rather a iterative and dynamic one that involves multiple steps and stages. You need to be familiar with the general framework and workflow of data analysis, such as the CRISP-DM (Cross-Industry Standard Process for Data Mining) or the OSEMN (Obtain, Scrub, Explore, Model, Interpret) models. You need to be able to define the problem or question, collect and prepare the data, explore and visualize the data, apply the appropriate models and techniques, interpret and communicate the results, and evaluate and improve the outcomes. You also need to be flexible and adaptable to changing requirements, expectations, and feedback, and be able to adjust and refine your analysis accordingly.
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Vishal Sethi
Executive Product Leader - Data, Analytics and AI
Some ways to get started 1) Get hold of explicit knowledge such as documented data dictionaries or database references 2) Get to talk with your predecessor or peers who are querying structured, unstructured, or semistructured data to understand deeper insights into the challenges involved with each data source 3) Learn as much about data first by means of simple querying and profiling your data before getting started
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Nam Dinh
Just started a company!
Data analytics is indeed an iterative and dynamic process that involves multiple steps and stages. Familiarity with a general framework or workflow, such as CRISP-DM or OSEMN, can provide a structured approach to guide the analysis process.
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Mehrdad Maghsoudi
Before pursuing a data analytics career, grasp the data analysis process and key components. Know data collection methods, data types (numerical and categorical), and data visualization tools like Excel or Python libraries. Develop a strong statistical foundation, including concepts like mean, median, and hypothesis testing. Be skilled in data analysis tools such as Python and SQL. Understand data cleaning and preprocessing, employ exploratory data analysis techniques, and learn data modeling (machine learning and statistics). Domain-specific knowledge is valuable, and hone communication skills to relay findings effectively.
Data analytics encompasses a wide range of techniques and methods that you can use to extract insights and value from data. You need to have a solid knowledge and understanding of the basic and advanced techniques, such as descriptive statistics, inferential statistics, hypothesis testing, correlation, regression, classification, clustering, association rules, sentiment analysis, text analysis, image analysis, and natural language processing. You also need to know how to choose and apply the right technique for the right problem, and how to interpret and evaluate the results and assumptions. You also need to keep learning and updating your skills and knowledge, as new techniques and trends emerge in the field.
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Nam Dinh
Just started a company!
Data analysts should possess a solid understanding of both basic and advanced techniques. The ability to choose and apply the right technique for the right problem, interpret and evaluate results, and continuously learn and update skills is essential for effective data-driven decision-making.
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Addy (Arya) Dash, MEng, CSPO®
Product Director @ Financial Services | Cloud, Big Data, ML
Assuming data preparation is done to an acceptable standard, there are still two key challenges to performing data analysis at scale (over terabytes or petabytes of data) that are holding back analytics service providers: 1) Compute and Cost: While cloud-based solutions like Google Colab, Azure Synapse, AWS Sagemaker, Snowflake, etc., have made it easy to access computing power, running analytics at scale in the cloud is still extremely expensive and sometimes prohibitive. 2) Model Selection: Whether you run a regression, clustering analysis, or a deep learning model, I have noticed that it is extremely difficult to convince all clients that a particular model (or chosen set of tuning parameters) is the best choice.
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Mehrdad Maghsoudi
Aspiring data analysts should start with Excel and SQL for foundational data manipulation and querying skills. Python and R open doors to advanced analytics and impactful data visualization. Proficiency in Excel and SQL is essential for entry-level roles, while mastering Python or R leads to specialized positions. Statistical software like SPSS and SAS provides additional analytical capabilities. A mix of these tools equips data analysts to tackle diverse data challenges, with Excel and SQL as core competencies and Python and R for complex analyses.
Data analytics is a broad and diverse field that offers various roles and career paths for different levels of experience and expertise. You need to be aware of the different roles and responsibilities that exist in data analytics, such as data analyst, data engineer, data scientist, business analyst, business intelligence analyst, data visualization specialist, and data consultant. You also need to know the skills and qualifications that are required and expected for each role, and how to showcase your portfolio and achievements. You also need to explore and network with the data analytics community and industry, and find the role and niche that suits your interests and goals.
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Nam Dinh
Just started a company!
It's the cool kid at the data party with a diverse crew of specialists. From number crunching data analysts to data wrangling engineers and the mad scientists of data science, this field has it all. Business folks, they've got their analysts and intelligence wizards. And don't forget the data viz artists turning numbers into eye candy. Plus, the data consultants who are the sage advisors of this data-driven world. But wait, there's more! To rock in this scene, you need skills, qualifications, and a killer portfolio. Be a network ninja, and find your groove in this data jungle. So, pick your role and let's get crunching those numbers, because data is the new black!
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Justin B.
AVP, Business Unit Compliance
I think this is already true for most jobs, but in the near future it will certainly be even more true, there will not be jobs that do not have some component of data analysis tied to them. I would not worry as much about finding a role in data analytics/analysis as I would about being prepared for when your role evolves into more of a data analysis role. If you already understand many concepts of data analytics, you will be “ahead of the pack” when your employer asks you to perform more tasks that are data related. I would take free or cheap courses to immerse myself in data analytics concepts now, so you are prepared for when your role evolves. Continuous learning is key.
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Fernanda Carneiro
Data Analyst | Business Analyst | Business Intelligence | Expert in Data Analysis, SQL, and Data Visualization
In data analytics, the same job might have different titles at different companies, and responsibilities can overlap. When job hunting, look at a variety of titles and read the job descriptions closely. Look for key skills they want and the tasks you'll do. Generaly speaking, Data Analysts interpret data; Data Engineers build systems; Data Scientists model trends; Business Analysts refine processes; Business Intelligence Analysts derive insights.
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Simon Jackson
Data Product Lead | I help product leaders accelerate growth with data
Don't expect a seat at the table. Being seen as a decision partner and doing the really impactful analytics work (beyond just running queries or building dashboards on request) has to be earned. You need to be invested in solving business problems - even when it means doing "non-data" work. So be mindful to pair your analytics learning with practical problem solving and business concepts.
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Ibrahím Israfilov
Data Science Consultant | MSc in Data Science | Microsoft Certified Data Analyst
Curiosity is a key 🔑 It is one of the main characteristics that the data analyst should have at the beginning/middle and end of the career 🏔 In terms of the technical skills For sure a solid knowledge of Excel would be a huge advantage before getting immersed into analytics career ⚔️
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Pushon Mukherjee
A good data analyst must not only possess strong analytical skills and adept at handling data, but should present their findings to the business in a simple way. And there are two skills that are important for this purpose - a solid understanding of the fundamentals of statistics/econometrics (not the math behind it but the objective of the technique) and a continuous appetite for learning about business processes. So, next time you present the output of a regression model to a sales leader, try to explain what "statistically significant" and "confidence interval" mean in your analyses and why they should be interested in them.