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What is Data Science?
Data science refers to the multidisciplinary approach of extracting actionable insights from large volumes of data collected. Gravitate has been crafting award-winning experiences on the web for clients in nearly every industry. Unlike most web design agencies
Preparing data for analysis and processing, conducting advanced data analysis, and presenting the results to highlight trends and allow stakeholders to make informed choices are all part of data science. The analysis of data is done by using tools such as algorithms, analytics, and artificial intelligence models.
Data science has become incredibly popular over the years due to the abundance of benefits it offers. For instance, data science helps to transform problems into research, then it comes up with practical solutions. You can also perform sentiment analysis with data science, which is essential for businesses who want to increase customer brand loyalty.
Data Science Life Cycle
One mistake that is often made when embarking on data science projects is rushing to collect data and analysing it without properly understanding the business problem at hand.
Therefore, by closely following each phase of the data science life cycle, it will ensure that the project goes smoothly and produces optimal results.
1 – Discovery
It is crucial to note the various details, requirements, priorities, and budget constraints before you begin a project. You must also be able to ask the appropriate questions.
This is where you determine whether you have the necessary resources in terms of people, technology, time, and data to support the project. Additionally, you have to frame the business problem during this phase.
2 – Preparation of Data
In this second phase, your data will undergo cleaning. The cleaning process helps to remove errors from the dataset such as blank columns, missing values, and wrongly formatted data. With clean data, you will be able to make better predictions.
Also, prior to modelling, the data must first be studied, analysed, and conditioned. This allows you to spot outliers and establish a relationship between different variables.
3 – Model Planning
Model planning refers to the process where you need to identify the techniques and methods you will utilise to draw a relation between variables.
There are various statistical formulae and visualisation tools that can be used, and some of the common ones include R, SQL Analysis Services, and SAS/ACESS.
Once you have drawn insights from your data and determined which algorithm to use, you will proceed to apply the algorithm and build a model.
4 – Model Building
At this stage, data sets are equally distributed for training and testing purposes. Techniques such as classification, association, and clustering are applied to training data sets to build the model.
Once the model is prepared, it is then tested against the testing dataset.
5 – Operationalise
The final model is eventually delivered with technical documents, codes, and reports.
On top of that, the model is thoroughly tested. If it passes the test, it will be implemented in a real-time production environment. By doing so, it helps to provide a clearer picture of the performance and other constraints on a smaller scale before fully deploying it.
6 – Results
In this last phase, it is important to assess if you have been able to find a solution to the business problem that you had framed in the first phase.
You will need to identify all the key findings, relay them to the stakeholders and finally, decide whether the results of the project are a success or a failure according to the criteria developed in stage one.
Careers Associated with Data Science
With businesses rapidly generating and collecting large volumes of data, their need for data scientists increases as well. In recent years, though the demand for data scientists is high and continuously growing, employers struggle to fill the job vacancies as the supply is low. This discrepancy has led to companies offering higher salaries than average to attract more applicants.
So, if you are keen on pursuing a career in data science, below is a list of jobs associated with the field.
1 – Data Scientist
Data scientists deal with big quantities of data to produce relevant and informed insights for the business using different sorts of algorithms, methods, and processes. They often use programming languages such as Python, R, and SQL.
2 – Data Engineer
Similarly, a data engineer works with large amounts of data. Their responsibility includes maintaining, building, developing, and testing structures such as large scale databases and processing systems. They also deal with programming languages like Python and Java.
3 – Business Analyst
Business analysts play an important role in improving business processes. They function as a bridge between the tech department and the business executive. Python, Tableau, and SQL are some of the programming languages they have to work with.
4 – Data Analyst
Data analysts have to mine extensive amounts of data. They then search for patterns, trends, and relationships in order to visualise compelling information and report it. The information is later used for further analysis to make business decisions. Like the previous few jobs, data analysts also deal with Python, SQL, and R. This is why learning advanced data analytics helps every analyst.
Conclusion
Data science jobs are not only limited to tech companies, but sectors like e-commerce, banking, and the health industry all make use of data science to continuously upgrade their systems.
With the proliferation of data and the vast number of job opportunities available, there has never been a better time to pick up skills in data science.
If you are keen on pursuing a career in data science, there are various ways to go about it. For instance, data science courses organised by companies such as Vertical Institute are a great way to earn a certification, if you have no prior educational qualifications related to the field.
Practicing popular programming languages such as Python and Java during your free time is also essential in continuously honing your knowledge and skills.
