Embark on Your Data Odyssey: Unveiling the Data Science Guidebook for Success!
Embark on Your Data Odyssey: Unveiling the Data Science Guidebook for Success!
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𝗔𝘀 𝘆𝗼𝘂 𝗮𝗹𝗹 𝗸𝗻𝗼𝘄, 𝗶𝗻 𝗼𝗿𝗱𝗲𝗿 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, 𝘆𝗼𝘂 𝗺𝘂𝘀𝘁 𝗵𝗮𝘃𝗲 𝘀𝗼𝗺𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝗸𝗶𝗹𝗹𝘀 𝗹𝗶𝗸𝗲 𝗠𝗮𝘁𝗵, 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀, 𝗮𝗻𝗱 𝗖𝗼𝗱𝗶𝗻𝗴… 𝗛𝗼𝘄𝗲𝘃𝗲𝗿, 𝗶𝗻 𝗺𝘆 𝗼𝘄𝗻 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲𝘀𝗲 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝗿𝗲 𝘀𝗼 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝗮𝗻𝗱 𝗰𝗮𝗻 𝗯𝗲 𝗱𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁 𝘁𝗼 𝗲𝘅𝗽𝗹𝗮𝗶𝗻 𝗶𝗻𝘁𝗼 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘀𝗸𝗶𝗹𝗹𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗰𝗮𝗿𝗲𝗲𝗿 𝗮𝘀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁.
How do I evaluate it?
From 1–5: Essential skills for every data scientist.
From 6–10: Depends on your position and tasks.
1. SQL, NoSQL Queries and Data Pipelines:
A question that almost anyone asks; is: Can the DBMS decide for itself what kind of action it should take?
Well, the simple answer is NO. Just like any other computer software, the DBMS needs a set of commands that can help it determine the nature of the task. This set of commands is provided by a computer language, which is SQL.
Before starting to install SQL and write commands to configure the database; We need to find the answer to the question: What are SQL and NoSQL? Please check my previous blog to know more about this.
Then why you must know it as a Data Scientist? Companies always prefer data scientists who can know more than just data modeling. That means they don’t have to hire more people to step in and build core pipelines. By doing that and also be able to gather your insights, improve your accuracy, write better reports, and have more interesting storytelling.
Sometimes, problems can be solved easily by just some queries. This will save you time and you won’t have to depend on data analysts or engineers.
Therefore, you must know how to write SQL or NoSQL queries to be a data scientist. There are no other ways around it.
2. Data Wrangling, Cleaning, and Feature Engineering:
Data is very important when understanding your situations, exploring new features, and building models… So, you must know to clean and wrangle your data.
Data Wrangling or data cleaning refers to processes to transform raw data into more ready-to-use formats. The method depends on the data and the goal you are trying to achieve.
According to Anaconda research, Data scientists spend about 45% of their time on data preparation tasks, including loading and cleaning data, according to a survey of data scientists.
It’s imperative to note that information wrangling can be time-consuming and burdening on assets, especially when done physically. This can be why numerous organizations organized approaches and best hones that offer assistance representatives streamline the information cleanup preparation. For this reason, it’s crucial to get the steps of the information wrangling handled and the negative results related off base or defective information.
Feature Engineering is a type of data wrangling that focuses on extracting features from unstructured data. It doesn’t matter whether you use Python or SQL to manage your data; you should be able to manipulate your data however you choose.
3. GitHub and Git or Version Management:
When I mention “version management,” I’m referring to GitHub and Git in particular. Git is the most widely used version control system, while GitHub is a cloud-based repository for files and folders. While Git may not appear to be the most straightforward ability to acquire at first, it is required knowledge for nearly every coding position.
Why? It enables you to collaborate and work on projects with others in real-time. It maintains track of all of your code’s versions (in case you need to revert to older versions).
4. Data Visualization and Storytelling:
The art of combining hard data with human communication to create an engaging narrative based on facts is known as data storytelling. It uses data visualization tools (such as charts and graphics) to assist the audience to understand the meaning of the data in a captivating and relevant manner.
The process of analyzing and filtering massive datasets to find insights and disclose new or different ways to understand the information results in a data-driven narrative. They’re made for a certain audience and consumed in a specific setting. This can help you transmit information or a point of view more effectively while putting the least amount of cognitive strain on your mind.
It’s one thing to create a visually attractive dashboard or a complex model that’s over 95% accurate. However, if you are unable to explain the importance of your work to others, you will not receive the recognition that you deserve, and you will not be as successful in your profession as you should be.
Storytelling refers to “how” you communicate your insights and models. Conceptually, if you were to think about a picture book, the insights/models are the pictures and the “storytelling” refers to the narrative that connects all of the pictures. Storytelling and visualization are severely undervalued skills in the tech field.
5. Regression and Classification:
Predictive modeling is the problem of developing a model using historical data to make a prediction on new data where we do not have the answer.
You won’t constantly be working on regression and classification models, i.e., predictive models, but it’s something that employers will expect you to know if you’re a data scientist.
Even if it’s not something you’ll do frequently, it’s something you’ll need to master if you want to develop high-performing models. And they are mission-critical models that had a substantial influence on the business.
As a result, you should know how to prepare data, use boosted algorithms, tune hyperparameters, and evaluate models using metrics.
6. People Skills, Business Skills and Domain Knowledge:
Please check out the blog “Why Business Skill Is Important In Data Field?” to know more about why I said modern data scientists must have people skills and business skills.
You have to know what you are doing, right? Precise and accurate problem definition is critical for the overall success of a data analysis project. Domain knowledge can often help us reach better precision and accuracy.
7. A/B Testing:
A/B testing is a form of experimentation where you compare two different groups to see which performs better based on a given metric. It also known as split testing, refers to a randomized experimentation process wherein two or more versions of a variable (web page, page element, etc.) are shown to different segments of website visitors at the same time to determine which version leaves the maximum impact and drive business metrics.
In the business sector, A/B testing is undoubtedly the most practical and commonly used statistical notion.
Why? A/B testing enables you to combine 100s or 1000s of tiny adjustments over time to produce major changes and benefits. A/B testing is crucial to grasp and learn if you’re interested in the statistical side of data analytics.
8. Clustering:
Clustering is a basic area of data science that everyone should at least be aware of. It is a key area of data science that everyone should at least be familiar with. Clustering is useful for a number of reasons.
You can find different customer segmentations, you can use clustering to label unlabeled data, and you can even use clustering to find cutoff points for models.
9. Recommendation:
One of the most useful applications of data science is the recommendation system. Because they have the ability to push revenue and profits, recommendation systems are extremely powerful. In fact, Amazon stated that their recommendation systems increased their sales by 29% in 2019.
As a result, if you ever work for a company where users must make decisions from a large number of options, recommendation systems may be a beneficial application to investigate.
10. Natural Language Processing (NLP):
Natural Language Processing, or NLP, is an area of artificial intelligence that focuses on text and speech. Unlike machine learning, I believe NLP is still in its infancy, which is what makes it so intriguing.
There are numerous applications for NLP:
- It can be used to conduct sentiment analysis to determine how people feel about a company or its product (s).
- It can be used to keep track of a company’s social media by distinguishing between positive and bad remarks.
- The foundation of chatbots and virtual assistants is natural language processing (NLP).
- Text extraction is another application of NLP (sifting through documents)
- Overall, natural language processing (NLP) is a fascinating and useful subset of data science.
hope you’ll love this guide
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