A data analyst requires an efficient strategy to solve data science problems. The work is not constrained to analysis and modeling, but it covers data interpretation  and cleaning of figures. It is requisite for a data scientist to possess strong analytical skills, magnificent communication ability, knowledge of data visualisation tools with a programming language, and a drive to find insights into numbers.

Workflow of the Data Science Project

Business Understanding

The target is clarified at this stage to gain a better understanding of the data science project.The quality of the questions decides the success rate of the project. An accurate understanding of the business will allow the collection of accurate figures and help in narrowing down the data acquisition part.

Analytic Approach

The data researchers will take a step forward toward solving the problem through the analytic approach. This is achieved after clearly stating the business problem in the first step. It includes a statistical and machine-learning explanation of the problem, as well as precise determination of trends required to solve the problem efficiently and effectively.

A predictive model is used when the concept of probability plays a role, while a descriptive approach depicts the relationship between variables. Every approach requires varied algorithms, and the best way to solve a problem that involves counts is through statistical analysis.

Data Requirements

The initial data collection is performed through essential data content, formats, and sources. This type of data is used inside the algorithm of the approach.

Data Collection

Based on the domain of the problem, the data sources available for the next step are identified. The data can be retrieved by using web scraping on a website or by using a repository with premade datasets.

From the above process, we can state that around 80% of the time is invested in collecting, cleaning, and performing feature engineering. To be more precise, most of the effort is in the formulation of the problem, identification of the current data sources, and understanding the type of questions or patterns to which an individual wants the answer. It is rightly stated, “Believe me, it’s not that hard.” “You just need patience and must know where to look.”

Data Understanding

The data science project for beginners is a stepping stone to success because it delves deeply into the data gathered in the previous process.Each data type is thoroughly checked for attributes and their names.

Data Preparation

In the next step, the analysis of the data is performed in a specific format. The data science project involves data preparation for modelling as one of the significant steps. It is because the model must contain filtered data with few errors or null values.

According to the observation, data scientists spend 80% of their time cleaning data and the remaining 20% providing insights and drawing conclusions.

Exploratory Data Analysis

It is another significant idea of a data science project that involves summarising the data to identify the structure, outliers, anomalies, and patterns in the data. The insights drawn from this step led to the building of the model.

Model Building

The process of modelling focuses on developing models of a descriptive or predictive nature. Predictive modelling is a process that covers data mining and the use of probability to anticipate outcomes.

If we have categorical data, the categorical variables are converted into dummy variables. The mean absolute error method is used for interpreting models as it is relatively easy to interpret and outliers do not have a greater impact.

Model Evaluation

The evaluation of a model in a data science project for beginners is performed in two ways: hold-out and cross-validation. The dataset is segregated into three subsets: a training set, a validation set, and a test set. Generally, the training:validation:test set ratios are 3:1:1, that is, 60%:20%:20% division among subsets.