Abhishek Vijayvargia’s Post

What specific aspects or questions do you have in mind about time series forecasting projects?

Writing a Good Job Description for Data Science/Machine Learning Things to do and things to avoid in order to find the right candidates for your open position Photo of a very good candidate by Thomas Butler on Unsplash I’ve probably been involved in the hiring process for data scientists a dozen times or more over my career, while never being the hiring manager myself, and I have been closely involved in writing the job description for several of these. It kind of seems like this should be easy — you’re just trying to convince people to apply for your job, so you can pick the one you like best, right? Well, it’s actually more complicated than that. Most of the people out there in the world are not qualified for any given job, and even among those who are qualified, there may be reasons they wouldn’t like working in this role. It’s not a one-way street; you don’t want just anybody to apply, you want the best suited people, for whom this job would work, to apply. So, how do you thread that needle? What should you write? This column is only my opinion and does not represent the views of my employer. I have not been involved in writing any job descriptions my current employer has posted, for ML or anything else. Why write a Job Description? To figure out what to write, let’s break down what it is a good job description is supposed to do, for a DS/ML job or for any other kind. Explain to candidates what the job is, and what they would do in the job Explain to candidates what qualifications you’re looking for in applicants These are the bare essential functions, although there are several other things your job description posting should also do: Make your organization seem like an attractive place to work for a diverse pool of qualified candidates Describe the compensation, work circumstances, and benefits, so candidates can decide whether to bother applying With this, we’re starting to get into more subjective and complicated components, in some ways. In some spots, I’m going to give advice for two different scenarios: first, for a small organization with few or zero existing DS/ML staff members, and second, for a medium or large sized organization with some DS/ML staff. These two can be quite different situations, with different needs and challenges in certain areas. You may notice I’m using “DS/ML” a lot in this article — I consider the advice here good for people hiring data scientists as well as those hiring machine learning engineers, so I want to be inclusive where possible. Sorry it’s a little clunky. What is this job? Firstly, for any organization, consider what kind of role you have open. I’ve written in the past about the different kinds of data scientist, and I’d strongly recommend taking a look and seeing what archetypes your role fits into. Think about how this person will fit into your organization, and be clear about that as you proceed. The Small Organization A challenge, especially for small organizations with ...

Top 5 Data Science Jobs in 2024 In the Fast Paced world of Technology, Data Science has emerged as a Leading field people want to Pursue, offering Tremendous career prospects. With the increasing reliance on data driven decision making, the demand for skilled Data Scientists is at an all time high. If you’re considering a career in Data Science, it’s essential to explore the Top Job roles that will be in demand in 2024. This blog post highlights the Top 5 Data Science Jobs in 2024 that promise exciting opportunities and growth potential. https://lnkd.in/ek9RkJnP #Hiring #Jobs #AI #ML #AIJobs #MLJobs #AImployed #Machinelearning #AImployedLaunch #AIRevolution #FutureOfWork #AIJobs #CareerDevelopment #TechCommunity #JoinUs #UnlockYourPotential #AIEngineering #TechInnovation #DataDriven #AIInnovation #DigitalTransformation #TechLeadership #TeamCollaboration #AIPrototyping #GrowthConsultancy #TechCareer 

Today Will Share Some thoughts to some or our beginner Students & Proffesionals about ( What is Data-Science 👀👩🏫) So Lets have A short Vedio About Data-Science⏳ Introduction to Data Science for Freshers✍️👩💻 Data science👩💻 is a multidisciplinary field that combines knowledge from various domains such as statistics, computer science, and domain expertise to extract insights and knowledge from data. As a data scientist, your primary role is to analyze large volumes of data to uncover hidden patterns, correlations, and trends that can be used to make informed business decisions, optimize processes, and solve complex problems. 1. ✍️Statistics and Mathematics⏳: Understanding statistical concepts like probability, hypothesis testing, and regression analysis is crucial for making sense of data and deriving meaningful insights. 2. Programming👩💻📔: Proficiency in programming languages like Python, R, and SQL is essential for data manipulation, analysis, and visualization. Additionally, familiarity with libraries and frameworks such as pandas, NumPy, matplotlib, and scikit-learn is highly beneficial. 3. Data Wrangling🖲: Data rarely comes in a clean and structured format. Data wrangling involves cleaning, transforming, and preparing raw data for analysis, which often constitutes a significant portion of a data scientist's workflow. 4. Machine Learning🧮: Machine learning algorithms enable data scientists to build predictive models and make data-driven decisions. Understanding various machine learning techniques like supervised learning, unsupervised learning, and deep learning is fundamental in data science. 5. Data Visualization🗞:Communicating insights effectively is essential in data science. Data visualization tools and techniques help in presenting complex data in a clear and understandable manner to stakeholders. 6. Domain Knowledge📘📔:Having domain-specific knowledge is beneficial for understanding the context of the data and deriving actionable insights relevant to specific industries or domains like healthcare, finance, e-commerce, etc. Roles and Responsibilities of a Data Scientist:🤷♀💬 - Collecting, cleaning, and preprocessing data. - Exploratory data analysis to understand the underlying patterns and relationships. - Building and evaluating machine learning models for prediction and classification tasks. - Communicating findings and insights to stakeholders through reports, presentations, and data visualizations. - Collaborating with cross-functional teams to solve business problems and drive data-driven decision-making. Career Opportunities in Data Science:👩💻 - Data Scientist - Data Analyst - Machine Learning Engineer - Business Intelligence Analyst - Data Engineer - Data Architect #Python #bigdata #sql #machinelearning #datawrangling #data #eda #numpy #pandas #artificialintelligence

What factors contribute to the low number of people choosing to become data scientists? Is there still a high demand for them in the job market? The relatively low number of people choosing to become data scientists can be attributed to several factors, while the demand for data scientists remains high in the job market. Here's a detailed look at both aspects: Factors Contributing to the Low Number of Data Scientists Educational Requirements: Advanced Education: Data science often requires advanced degrees (Master's or Ph.D.) in fields like statistics, computer science, or data science itself. The need for specialized education can deter potential candidates. Complex Skill Set: The field demands proficiency in a wide range of skills including programming, statistical analysis, machine learning, and domain expertise. Acquiring and mastering these skills can be challenging. Technical Complexity: High Technical Barriers: Data science involves complex mathematical and computational techniques that can be intimidating. The steep learning curve associated with these techniques can be a deterrent. Continuous Learning: The rapidly evolving nature of technology means that data scientists must continuously update their skills and knowledge, which can be a significant commitment. Lack of Awareness: Unawareness of the Field: Many potential candidates may not be fully aware of what data science entails or may have misconceptions about the role. This can result in a lack of interest or enthusiasm for pursuing a career in this area. Educational Gaps: Limited exposure to data science concepts in early education or undergraduate programs may contribute to fewer students choosing to specialize in this field. Workplace Realities: Job Demands: Data science roles can involve long hours, high-pressure environments, and challenging projects. The nature of the work may not appeal to everyone. Job Expectations vs. Reality: The actual day-to-day work may differ from what is often portrayed in media or job descriptions, leading to disillusionment for some. Economic and Regional Factors: Economic Barriers: The cost of education and training in data science can be high, which may discourage individuals from pursuing this career path. Regional Disparities: In some regions, there may be fewer educational opportunities or job prospects related to data science, limiting the pool of potential candidates. Demand for Data Scientists Despite the challenges associated with entering the field, the demand for data scientists remains robust and is expected to continue growing. Several factors contribute to this high demand: Data Explosion: Increased Data Generation: The exponential growth in data generated by businesses, social media, IoT devices, and other sources creates a need for professionals who can analyze and derive insights from this vast amount of information.

Dear Data enthusiasts, 🚀Starting Your Data Science Career? Here's the REAL Roadmap to Success! If you're starting your journey in Data Science, you might think mastering Python, ML algorithms, SQL, a bit of Pandas, Seaborn, and some deep learning concepts is enough. But here's the truth 👇 🚨 Don’t overlook Pandas and Seaborn! 🚨 Many focus heavily on advanced ML algorithms, stats, and visual tools like Power BI or Tableau, assuming they’ll be ready for a Data Scientist role. But the foundation of real-world data work? Pandas and Seaborn. 📊 Why Pandas is Non-Negotiable:- 80% of a Data Scientist’s work involves data cleaning and wrangling. Without strong Pandas skills, handling raw data can be challenging. Pandas offers essential techniques, like:- 1️⃣ filtering data 2️⃣ grouping with GroupBy 3️⃣ merging with concat 4️⃣ handling missing data with fillna 5️⃣ renaming columns, astype(), apply(), and much more! These are not just "nice-to-have" skills; they're must-haves for efficient data cleaning and processing. 🚀 📈 Seaborn for Data Visualization:- Seaborn lets you create visually appealing and insightful plots, helping you answer specific questions and uncover trends. It’s not just about making charts; it’s about making meaningful insights visible. 💼 Pro Tip for Freshers:- Companies prioritize fundamental skills over advanced topics. Before diving deep into machine learning or complex algorithms, make sure you have a solid foundation in Pandas, Seaborn, and data manipulation. This will make your work far more effective and valued. 👣 Take one step at a time:- Once you're confident in the fundamentals, you’ll be ready to explore advanced topics! 👉 Remember, strong foundations make for strong data scientists! [Ref : Image Generated by AI] 🏷️Tagging some amazing people in the data science community Priyanka Banerjee RASHMIKA S Adarsh srivathsa M S Karina Samsonova Mohit Chaudhary SAHANA K M Neetu . Manali Kulkarni Akash Narang Dixit Jadav Shveta Maini Aman Pandey let's continue to grow the data science community! 💬 👉Follow Korrapati Jaswanth more insights and content on DS/ML. #DataScience #Pandas #Seaborn #DataCleaning #MachineLearning #CareerTips #DataVisualization #DataScientistJourney

🚀 Seeking Full-Time Opportunities in Data Science & Analytics 🚀 With 1 year of hands-on experience as an Apprentice - Data Scientist & Data Analyst at SAC - ISRO, I have developed a strong foundation in Machine Learning, Data Analytics, and Satellite Telemetry Data. Throughout this year, I’ve worked on solving complex, data-driven challenges, delivering impactful solutions that drive operational efficiency and decision-making. I am now looking to transition into full-time, entry-level roles as a Data Scientist or Data Analyst in leading product-based companies, where I can continue to apply my skills and contribute to data-driven innovation. Key Achievements & Expertise: Machine Learning: Developed predictive models from satellite telemetry data, supporting data-driven decision-making and operational improvements. Data Analytics & Visualization: Proficient in Python (NumPy, Pandas, Scikit-learn), Excel, SQL, Power BI, and Tableau to extract insights and create impactful visualizations for business analysis. Data Preprocessing & Transformation: Skilled in cleaning and transforming complex datasets, preparing them for analysis and model development. Model Evaluation & Optimization: Applied techniques like ROC curves, confusion matrices, and feature importance to enhance model performance and transparency. Collaboration & Communication: Effectively communicated complex data findings to non-technical stakeholders, enabling data-driven decision-making. Industries of Interest: Top IT Companies: Google, Microsoft, Amazon, IBM, NVIDIA, Adobe, Intel Corporation, Apple, etc. Banking & Financial Services: Goldman Sachs, JPMorganChase, Morgan Stanley, Citi, Deutsche Bank, Barclays, HSBC, Standard Chartered, etc. Defense & Aerospace: Boeing, Northrop Grumman, Raytheon, Airbus, GE Aerospace, TATA Advanced Systems Limited, Mahindra Aerospace, etc. Technical Skills: Machine Learning: Model Development, Evaluation, Optimization Data Analytics: Data Preprocessing, Feature Engineering, Insights Extraction Data Visualization: Excel, Power BI, Tableau, Python (Matplotlib, Seaborn) Programming: Python (NumPy, Pandas, Scikit-learn), SQL Communication: Data Storytelling, Reporting, Stakeholder Engagement After 1 year of hands-on experience at SAC - ISRO, I am eager to bring my expertise in data science and machine learning to a full-time role at an innovative, high-growth organization. Let’s Connect! If you're looking for a passionate, skilled data science professional with a solid technical foundation, I would love to connect and explore how I can contribute to your team. Feel free to reach out via LinkedIn or email at bhupendrasuryawanshi1507@gmail.com. #DataScience | #MachineLearning | #Analytics | #DataDriven | #Python | #Excel | #SQL | #PowerBI | #Tableau | #DataScientist | #DataAnalyst | #Innovation | #CareerOpportunities | #FullTimeJobs

6 In-Demand Data Science Jobs You Don't Want to Miss in 2024 The data science field is booming, offering exciting opportunities for those with the right skills and knowledge. Here's a look at six of the most in-demand data science jobs in 2024: 1. Machine Learning Engineer: These architects of the future design and build AI programs that can learn and make decisions based on massive datasets. They work closely with data scientists and programmers to create intelligent systems that automate processes and personalize user experiences. 2. Data Analyst: Data analysts are the information detectives of the data science world. They collect, clean, and analyze data to identify trends, patterns, and insights that inform business decisions. Strong communication skills are key, as data analysts need to translate complex data findings into actionable recommendations for stakeholders. 3. Business Intelligence Analyst: Business intelligence analysts leverage data to provide businesses with a clear picture of their current performance and future potential. They use data visualization tools to create dashboards and reports that track key metrics, identify areas for improvement, and support strategic decision-making. 4. Data Scientist: The data scientist role is the jack-of-all-trades in the data science field. They possess a blend of statistical, programming, and business acumen to tackle a wide range of challenges. From building predictive models to designing experiments, data scientists extract knowledge from data to solve complex problems and drive innovation. 5. Deep Learning Engineer: Deep learning engineers are the masterminds behind cutting-edge AI systems. They specialize in building complex models using deep neural networks, a form of artificial intelligence loosely inspired by the human brain. Their expertise allows them to create intelligent systems capable of tasks like image recognition, natural language processing, and self-driving cars. 6. Data Security Analyst: As the volume and complexity of data grows, so does the need to protect it. Data security analysts play a critical role in safeguarding sensitive information from cyber threats. They develop and implement data security measures, monitor for suspicious activity, and ensure data privacy compliance. What skills do you need to land your dream data science job? Regardless of the specific role, strong analytical thinking, programming proficiency (especially Python), and excellent communication skills are essential. Additionally, domain-specific knowledge can be a major advantage. Are you ready to join the data science revolution? Share your thoughts and career goals in the comments below! #DataScience #DataAnalytics #MachineLearning #AI #FutureofJobs #ITEBsAcademy

Data Analyst to Data Scientist: Your Career Transition Guide for Essential Skills and Steps Data science is one of the most in-demand and rewarding fields in the 21st century, as it involves using data to solve complex and impactful problems. Data science requires a combination of skills, such as statistics, programming, machine learning, and communication. For data analyst to data scientist career transition, you may wonder what steps you need to take, and what skills you need to acquire. In this article, we will provide you with a career change guide, and help you make a smooth and successful transition to Data science. What is the difference between a data analyst and a data scientist? Before we dive into the career change guide, let us first understand the difference between a data analyst and a data scientist. Data analyst and data scientist are both data-related roles, but they have different scopes, responsibilities, and skill sets. Data analyst: A data analyst is someone who collects, cleans, and analyzes data, using tools such as Excel, SQL, and Tableau. A data analyst’s main goal is to provide insights and reports based on the data and answer business questions. A data analyst typically works with structured and well-defined data sets and follows a predefined process or methodology. Data scientist: A data scientist is someone who applies advanced techniques, such as machine learning, deep learning, and natural language processing, to data, using tools such as Python, R, and TensorFlow. A data scientist’s main goal is to create predictive and prescriptive models based on the data and solve complex and novel problems. A data scientist typically works with unstructured and diverse data sets and follows an exploratory and experimental approach. What skills do you need to become a data scientist? If you are a data analyst who wants to become a data scientist, the following are the skills required for the Data Scientist role: Programming: Programming is the foundation of data science, as it allows you to manipulate, process, and analyze data, as well as implement and deploy machine learning models. You need to learn one or more programming languages, such as Python, R, or Java, and be familiar with their syntax, data structures, libraries, and frameworks. You also need to learn how to use tools such as Jupyter Notebook, GitHub, and Google Colab, to write, share, and run your code. Statistics: Statistics is the core of data science, as it provides you with the concepts, methods, and techniques to understand, interpret, and communicate data. You need to learn the basics of statistics, such as descriptive statistics, inferential statistics, hypothesis testing, and confidence intervals, as well as advanced topics, such as regression, ANOVA, and Bayesian statistics. You also need to learn how to use tools such as NumPy, SciPy, and scikit-learn, to perform statistical analysis and modeling. Analytics insight

Here is the another Academy Podcast on "Tips to Crack a Data Scientist Interview" by Abhishek Das sir, Senior Data Scientist at Aptus Data Labs. 🚀 Data Science: It is all about leveraging data to bring value to the business. 📌 Roles in Data Science: There are mainly 4 roles in Data Science 👉 Data Engineer: - Most companies haven't adopted data science, so they look for this role. The main work involves setting up the infrastructure for data science. - Skills required: SQL, Python, Statistics 👉 Data Analyst: - Focuses on extracting data, visualization, and providing insights. The main work includes fundamental analysis: getting data from databases, visualizing it via BI tools, and explaining it to stakeholders. - Skills required: SQL, Python 👉 Data Scientist: - Focuses on using ML and DL algorithms for optimization and prediction. Builds and deploys models and requires knowledge of cloud infrastructure for deployment. - Skills required: SQL, Python, Statistics 👉 ML Engineer: - Focuses on researching and optimizing algorithms. The main work involves creating new algorithms and improving existing ones. - Skills required: familiarity with ML libraries and frameworks, strong problem-solving and data analytical skills. 📌 Tips to crack for Data Science roles 🚀 👉 For aspiring Data Scientists, acing the interview process is key. Freshers, pay attention! Here's what you need to know: - Prepare for a two-round interview: coding to test SQL and Python skills, followed by a theoretical round on statistics and ML algorithms. - Remember, fundamentals matter. Impress at every touchpoint with the company, be it email, phone calls, or in-person interactions. 👉 Crafting your resume? Keep these elements in mind: - Skills: Highlight your top 5 strengths with proficiency levels. Authenticity is crucial – interviewers can differentiate genuine skills from embellished ones. - Projects: Showcase relevant projects that showcase your expertise. Interviewers look for key skills and project types. 👉 Try these tips for a better job search: - Don't just look at big names; there are lots of great jobs and salaries at smaller companies. - Use LinkedIn to enhance your profile and by adjusting filters, you can identify target industries, companies, and roles. - You can gain insight into hiring managers through LinkedIn. Get connected, network, and seek referrals to increase your chances. Level up your Data Science game with these strategic tips! 💡 Thank you Avinash Dara sir and Abhishek Das sir for this amazing session. #Nxtwave #Ccbp #DataScientist #CcbpAcademy #DataScience #Podcast #MasterClass #Ccbpian #NxtWaveAcademy #RahulAttuluri #DataEngineer #DataAnalyst #MLEngineer #MachineLearning #ML #Python #SQL #ProblemSolving #Skills #JobHunting #LinkedIn #JobTips #LinkedInTips #JobOpportunities #Networking #CareerGrowth