In the field of computer science, data science has been popular for many years. Recently, Data science programmes are being offered by many universities.
The need for data science and big data specialists is increasing dramatically as more data is being generated by vibrant services and applications. The field of data science has emerged as a fantastic career choice for software developers and data nerds.
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What is data science?
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Data science has an intersection with artificial intelligence but is not a subset of artificial intelligence.
Data science is the study of an aroused curiosity in any given field, the extraction of data from a large source of data related to the question in mind, processing data, analyzing and visualizing this data, so as to make meaning out of it for IT and business strategies.
In simple terms, it is understanding and making sense of data. A lot of tools are used in data science. They include statistical tools, probabilistic tools, linear and metric algebra, numerical optimization and programming.
Career paths in data science.
One of the lucrative and in-demand professions for qualified experts is data science. Although a job in data science is fulfilling and lucrative, getting started is not that easy. It is not necessary to have a master’s or bachelor’s degree to work in data science. One requires the proper skill set and expertise. Below are examples of career choices in the field of data science:
- Data Analyst
- Data Scientist
- Data Engineer
- Machine Learning Engineer
- NLP Engineer
- Business Analyst
- Power BI engineer
Steps involved in developing a suitable machine learning model
Data collection: This is basically find a suitable dataset to work on, you can either import a csv, excel or a json file.
Data Preparation: Putting together all the data you have and randomizing it, Cleaning the data to remove unwanted data, missing values, rows, and columns, duplicate values, data type conversion, Visualize the data to understand how it is structured and understand the relationship between various variables and finally Splitting the cleaned data into two sets - a training set and a testing set.
Choose suitable model: Depending on the type of data you’re working with to solve a regression or a classification problem. Can be linear or logistic regression, SVM, XGBoost, LSTM , BiLSTM , Random Forest and so on.
Training the model: you pass the prepared data to your machine learning model to find patterns and make predictions.
Model Evaluation: After training your model, you have to check to see how it’s performing. This is done by testing the performance of the model on previously unseen data.
Parameter Tuning: Adjusting to see how best to improve the accuracy of the model.
Make Predictions: In the end, you can use your model on unseen data to make predictions accurately.
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