Learning any technology is not easy. At the starting it is so overwhelming that many quits. Data Science is no exception.
Here are some of the major problems faced people transitioning to data science or aspiring to be a data scientist.
If you keep these points in mind while learning data science it will help you lot to overcome those problems. As these problems are faced almost all the new learners.
1. data preparation
Data scientists spend 80% of their time cleaning and preparing data to improve its quality make it accurate and consistent, before using it for analysis. However, 57% of them consider it was worst part of their jobs, labeling it the such a time-consuming thing and dirty data . They are required to go through terates of data, across multiple formats, sources, functions, and platforms, on a day-to-day life according to Forbes. The title of this article is called ‘Most Time-Consuming, Least Enjoyable Data Science Task‘, so you can imagine why 13.2% of Data Scientists are looking for new jobs.
2.Multiple data sources
As organizations continue to use different types of apps and tools and generate different formats of data, there will be more data sources that the data scientists need to access to produce meaningful decisions. This process requires manual entry of data and time-consuming data searching, which leads to errors and repetitions, and eventually, poor decisions and can minimize or eliminate the need to create database table joins in an external data access tool. Using multiple data sources also enables measure allocation and giving instant access to Data Scientists and others that may need it. This saves Data Scientists a lot of time and effort, improving the overall workflow.
3.business problem understanding
this phase consists of a very precise specification of the problem together with methods of evaluating the achievement of the goal. Our goal is to create a model of the problem, which we use in turn to find a solution.
Before collecting data, wrangling it, and performing any form of analysis; Data Scientists have to understand the problem that needs to be solved. They are the scientists of data, so they will be the ones reconstructing it to find the solution; so it is imperative they have a good understanding of the issuse
Understanding the business problem is the first stage in a Data Science workflow. If the first stage of the workflow is correctly thought through, it can always be a reference point when Data Scientists move on to data preparation and analysis.
If you are willing to prepare for job at the same time with learning. Check this post to know the important topics often asked on interviews.
|While learning data science another thing is to keep pandas library in mind. In machine learning pandas play a crucial role in manupulating data and performing exploratory data analysis.|
4.communication with non-technical stake holders
Communicating your ideas to a non-technical audience can be really tricky. Whether it’s presenting to your non-tech team lead or communicating via email. This skill of translating technical insights into less jargon-filled, easily digestible nuggets is crucial for everyone, but data scientists should practice this skill more rigorously
Before performing data analysis and building solutions, data scientists must first thoroughly understand the business problem. Most data scientists follow a mechanical approach to do this and get started with analyzing data sets without clearly defining the business problem and objective.
Therefore, data scientists must follow a proper workflow before starting any analysis. The workflow must be built after collaborating with the business stakeholders and consist of well-defined checklists to improve understanding and problem identification.
How do I start learning data science?
Once you got to know the problems and challenges that you may face while learning data science. Now is time to get started. Follow these steps and start your journey to data scientist.
- Step 0: Figure out what you need to learn.
- Step 1: Get comfortable with Python.
- Step 2: Learn data analysis, manipulation, and visualization with pandas.
- Step 3: Learn machine learning with scikit-learn.
- Step 4: Understand machine learning in more depth.
- Step 5: Keep learning and practicing.
- Join Data School (for free!)
What are the basics to learn data science?
What skills do data scientists need to succeed?
- Programming in Python or R (either works)
- Fluency with popular packages and workflows for data science tasks in your language of choice. …
- Writing SQL queries.
- Statistics knowledge and methods.
- Basic machine learning and modeling skills.
How quickly can I learn data science?
You can learn Data Science fundamentals in approximately 6 – 9 months committing 6 – 7 hours a day. However, becoming a ‘good data scientist’ that can add value to a company within a high responsibility role will take years.
5 Best Free Data Science Online Courses to Learn in 2022
- An Introduction to Data Science [Udemy Free Course]
- Essentials of Data Science [FREE Udemy Course]
- What is Data Science? [Coursera FREE Course]
- Intro to Data for Data Science [FREE Udemy Course]
- Introduction to Data Science using Python [Free Course Udemy]
If you want to get a one on one personalised classes. Or help at any topics and have any doubt, feel free to click on the whatsapp icon on right 🙂