I am in my 4th year of bachelors and i don't have any technical skills but I'm interested to learn skills which are related to become a data scientist or analyst and I'm interested to take up jobs related to business analyst but i dont know where to start and what to study and how to apply .i need help. Can anyone guide me what to study and how to apply and give me a schedule of how many hours I should study to improve my skills

10 Comments

1. AnonymousAugust 24,2024 at 12:25PM UTC

   You'll need to discuss the various programs and schedules and lab hours with a university school counselor. They will have the details application process understanding.

2. AnonymousAugust 25,2024 at 4:43PM UTC

   We don't have a university counselor

3. RachelAugust 24,2024 at 12:28PM UTC

   Out of curiousity, what have you been studying during the 4years you've been there thus far? My school had guidance counselors that we were assigned to at the beginning. Can you ask them what a good plan would be?

4. AnonymousAugust 25,2024 at 4:42PM UTC

   We are not provided my such things so all we did was study the syllabus which they have given us but they never mentioned that we have to learn and develop i mean it's going to be half of our fault for not exploring but we just didn't know what to do without a guidance and when it's almost the end every recruiter wants a set of skills and we aren't able to satisfy those because of lack of knowledge related to what to take up

5. AnonymousAugust 24,2024 at 12:51PM UTC

   Check out Generation.usa for their online technical program. Also Google.

6. Abkhen BergAugust 24,2024 at 1:01PM UTC

   Learning data science involves acquiring a mix of skills in programming, statistics, and domain knowledge, along with hands-on experience with data.

   Start by brushing up on the foundational math and stats concepts, including probability, distributions, hypothesis testing, linear algebra, and calculus. These are essential for understanding data science algorithms.

   Learn SQL and programming in Python and the many libraries available. R can also be useful.

   Learn how to handle missing data, remove duplicates, and transform raw data into a usable format.

   Learn how to visualize data to find insights. Besides Matplotlib and Seaborn, you might also want to learn about tools like Tableau or Power BI if you want to be an analyst.

   Learn about different data analysis techniques: descriptive, diagnostic, predictive, and prescriptive analytics. Understand how to explore data, find patterns, and generate insights using exploratory data analysis (EDA).

   Learn Machine learning: Supervised Learning: Understand algorithms like linear regression, logistic regression, decision trees, and support vector machines.

   Unsupervised Learning: Learn about clustering (like K-means) and dimensionality reduction techniques (like PCA).

   Deep Learning: If you're interested, learn about neural networks using libraries like TensorFlow or PyTorch.

   Use publicly available datasets from sources like Kaggle, UCI Machine Learning Repository, or government data portals. Participate in Kaggle competitions to test your skills against other data scientists.

   Understand Data Science Tools and Frameworks like Jupyter Notebook, Git for version control, and cloud platforms like AWS or Google Cloud for deploying models. Frameworks for machine learning such as TensorFlow, Keras, or PyTorch.

   Start with simple projects and gradually increase complexity. For example, start with data cleaning and visualization projects, move to predictive modeling, and then complex machine learning projects. Document your projects and maintain a portfolio, which can be helpful for job applications.

   Follow data science blogs, podcasts, and forums like Towards Data Science, KDnuggets, or Reddit's r/datascience. Attend meetups, webinars, or conferences to network with other data science professionals.

   Consider online courses and certifications from platforms like Coursera, edX, Udacity, DataCamp and/or DataQuest.

   Be patient and consistent, you'll gradually build the knowledge and skills required to become proficient in data science. Hands-on practice and real-world application are key, so focus on doing as much as learning. Learning data science involves acquiring a mix of skills in programming, statistics, and domain knowledge, along with hands-on experience with data.

   There are no accredited certifications, and it would help if you get a Masters in DS or Analytics. I love going to work every day, it is an interesting and knowledge filled profession. Good luck.

7. AnonymousAugust 25,2024 at 4:39PM UTC

   This is going to help me a lot and thankyou for sharing your experience !

8. Mary WehrleAugust 25,2024 at 2:14PM UTC

   Use this website. I put in data analyst for the search. There is a list of possible positions. Each one with a sun, has a positive outlook for a growing industry.

   If you look into each position, there will be a general description of duties and required education and experience. This will help target you education. Hope this helps

   https://www.onetonline.org/find/result?s=Data+analyst+

9. AnonymousAugust 25,2024 at 4:37PM UTC

   Thanks a lotttt!

10. William HarrisSeptember 17,2025 at 10:15AM UTC

    The NCA-6.10 exam validates knowledge of Nutanix enterprise cloud solutions. It covers fundamental concepts like infrastructure management, cluster deployment, storage, and virtualization. The https://www.dumpstool.com/NCA-6-10-exam.html certification suits IT professionals beginning their journey in hyperconverged infrastructure. Passing NCA-6.10 highlights your foundational skills, opening doors to advanced Nutanix certifications and cloud technology career opportunities.