The Way of Data, Science: For Beginners 2023
Tags: datascience
Published: 2023-10-01 20:41:02

Want to venture into the world of data? While there is no specific path that must be follwed, some knowledge and skills must be gained along the way. Let's start with the most important;

The prerequisites:
Mathematics 
- understanding the basics of statistics and probability will save you a lot of headaches down the road. There are a lot of free resources available for this in youtube and publications. Understanding calculus and linear algebra is also crucial sepecially when you want to dive in the areas of machine learning later on.

Programming
- having problem solving skills using programming is another tool one must master to be successful in data science. The most popular language for this is Python (recommended for beginners) and it's pretty easy to pick. R, Java, Scala are also options for data science with the last two mostly used in building of data platforms and pipelines.

Data definition and manipulation using SQL 
- most of the data you will be dealing with will be stored in a database. Now, this isn't always relational databases, but even the NoSQL database tend to have an interface where SQL can be used to query data. This makes the knowledge of how to interact with storage systems using SQL invaluable for a data scientist.

Once you have mastered this, the most interesting part of the journey begins, specific knowledge of data science. This includes the following steps; 

Data collection and cleaning 
- consolidate data from various sources cleaning it, removing outliers and transforming it into a format that can be used in the chosen analytical tools.

Data understanding:
- exploratory data analysis: is the process of visually and statistically summarizing, interpreting, and understanding the main characteristics of a dataset. It involves generating summary statistics, visualizing the data distribution, identifying patterns, and uncovering relationships or anomalies. EDA helps data scientists gain insights into the underlying structure of the data and informs decisions about further analysis or modeling.

- descriptive statistics: involve using numerical summaries to describe the main features of a dataset. This includes measures of central tendency (mean, median, mode) and measures of variability (range, variance, standard deviation). Descriptive statistics provide a high-level overview of the dataset, helping to understand the typical values, spread, and distribution of the data.

Data visualisation
- Interact with tools for transforming data into visually appealing and understandable representations which can be used to communicate findings to stakeholders.

Data modelling

![Commander Data (Star Trek)](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/v0dxwu2gqrq4vrctv2s4.png)


- Machine learning falls in this phase. It allows computers to learn from data without being explicitly programmed. This involves fitting models to data to identify patterns in the data and make predictions and recommenditions based on the learned knowledge of the data.

Dashboard and deployment 
- A dashboard is a visual representation of data that provides a quick, clear, and concise overview of key performance indicators (KPIs) or metrics relevant to a particular business or process. 
- Deployment in data science refers to the process of taking a machine learning model or an analytical solution and making it available for use in a production environment. This could involve integrating the model into a web application, making it accessible through an API, or incorporating it into a larger software system.

Reporting 
- This is the communication of your findings to the stakeholders. Data science relies heavily on communication to drive business needs with a data oriented approach. A data scientist must be able to share their findings and their relevance to the decision makers to extract value from data and data operations.

One these basics are a part of you, make sure you build projects to reinforce this knowledge and showcase your skills. Enough words have been said, time to actually put in the work. Good luck