Data Science Roadmap 2023-2024
Published: 2023-10-04 07:29:49

What is data science? and what is the difference between data analysis and data science?

- Data Science is a field that focuses on extracting knowledge from massive data sets. It combines skills from Mathematics, statistics, specialized programming, advanced analytics, AI, and machine learning. 
- Data Analysis is the process of collecting, cleaning, transforming, and modeling data for decision-making purposes.

The difference between data analysis and data science is
- An analyst works on understanding data and identifying trends while a scientist works to create frameworks and algorithms for data collection

A learning road map can be defined as a strategic plan with various steps to achieve a desired goal. In this case, you will get to understand different verticals and areas to focus on for a beginner in data science.

**<u>The Data Science Life-Cycle</u>**
It revolves around the use of machine learning and different analytical strategies to produce insights and predictions from data for a set objective.
The lifecycle involves the following steps:

1. **Business Understanding**- It is essential to understand the business goal for the aim of the analysis. After desirable perception is only when we can set the precise aim of the evaluation that is in sync with the business objective. For example, does the business want predictions on product performance or customer churn? etc.

2. **Data Understanding** -This is the next step after business understanding. This includes a series of all reachable data. This step includes describing the data, their structure, their relevance, and their record type. Basically, it's extracting any data that you can get about the information through simply exploring the data.

3. **Data Preparation**- This includes steps like choosing the applicable data, integrating the data by means of merging the data sets, cleaning it, treating the lacking values through either eliminating them or imputing them, treating inaccurate data through eliminating them, additionally test for outliers the use of box plots and cope with them. Constructing new data, derive new elements from present ones. Format the data into the preferred structure, and eliminate undesirable columns and features. This step is the most time-consuming but arguably the most essential step in the complete existence cycle.

4. **Exploratory Data Analysis**- In this step, we get some concept about the answer and the elements affecting it. The distribution of data inside distinctive variables of a character is explored graphically by the use of bar graphs. Relations between distinct aspects are captured via graphical representations like scatter plots etc. 

5. **Data Modeling**- In this step, a model takes the organized data as input and gives the preferred output. This step consists of selecting the suitable kind of model, whether the problem is a classification problem, a regression problem, or a clustering problem. After deciding on the model family, amongst the number of algorithms in that family, we pick out the algorithms to put into effect and enforce them. We need to tune the hyperparameters of every model to obtain the preferred performance. We also need to make sure there is the right stability between overall performance and generalizability. 

6. **Model Evaluation**- The model is evaluated to check if it can be used for deploration. The model is examined on unseen data, and evaluated on a cautiously thought-out set of assessment metrics. The model assessment helps us select and construct an ideal model.

7. **Model Deployment**- The model after a rigorous assessment is at the end deployed in the preferred structure and channel. This is the last step in the data science life cycle. 

**Key Tools for Data Science**

- Programming languages such as Python, R, and SQL. One can choose one of the languages and learn. Most of the tutorials can be found online and practice projects.

- Machine Learning libraries like TensorFlow, Keras, and Scikit-learn.

- Data Visualization tools such as Tableau, PowerBI, MatplotLib, GGplots, etc. One can choose to use any of them depending on the programming language used.

- Data storage and management systems like MySQL, MongoDB, and PostgreSQL. 

In conclusion, it is best for a learner to set their desired goal on why they want to study and focus on data science and how this could be beneficial to them. It is important to do proper research to evaluate your strong fields and put them to use.
Happy learning!