Data science is the practice of extracting value from data through artificial intelligence (AI), machine learning, and statistics. Using data science tools, businesses can generate valuable insights that they can use to make better decisions and optimize existing products and services.
The data science process has many components: data mining, data cleaning, exploration, predictive modeling, analysis, and data visualization. Data scientists use different languages and tools such as Python, Java, R, and SQL to create the project pipelines that best fit the requirements. Companies also use Apache Spark for big data and Tableau/Datapine for business intelligence and visualization.
Many organizations use automation tools for capturing and combing through large data sets. Version control tools are used to mark project changes and keep track of the modified data. Finally, the data is sent to data engineers/scientists who clean and preprocess the data. They remove duplicate or irrelevant entries and filter outliers. They may also need to handle missing data.
Hiring Guide
After proper processing, data scientists carry out hypothesis testing and predictive modeling through machine learning algorithms. To fully understand the data and generate insights, they may also need to include statistics and probability. Algorithms used at this stage include decision trees, linear and logistic regression, classification, and XGBoost.
They may also need to use SQL queries to join the data through databases such as MySQL and PostgreSQL. The last step is data presentation. This is done through charts and reports. Engineers use data visualizations tools such as Tableau and R Studio to create dashboards and produce reports
Data science in today’s market
Nowadays, data science is integral to an organizations’ decision-making process. Its popularity has grown over the years, with multiple companies funding and implementing data science projects. Even during the COVID-19 lockdown, when most businesses were impacted, companies invested heavily in data and decision sciences.
Data science projects improve the efficacy of the existing applications by generating a diverse set of insights about customers, markets, and businesses. They can be used to create recommendations and detect fraud. Furthermore, data science also assists companies’ branding and marketing initiatives by segregating highly specific consumer groups for laser-precision campaigns.
Issues companies have when hiring data science engineers
Although data science is a thriving field, companies still have difficulty hiring data science engineers/scientists. There is a huge skill gap in the industry. One reason for that is the amount of work required just to stay in the field. Data science requires a lot of upskilling and specialization, and many engineers are unable to keep up with the constant training.
Another major issue that companies face while hiring data scientists is their inexperience in cleaning data. Data scientists spend a huge amount of time cleaning and preprocessing data. It means cleaning up inaccurate, duplicate, incomplete, and inconsistent entries. This requires a lot of patience and experience, along with business knowledge, which many candidates lack.
How to select the perfect data science engineer?
While selecting a data scientist may seem difficult, there are certain things you can check for before hiring data scientists. The prospective candidates must possess statistical and probability knowledge and should have experience with machine learning.
They should also have experience in data engineering and visualization tools. They should be well versed in SQL and query handling. Candidates with knowledge of big data tools such as Apache Spark should be preferred.
Finally, data visualization is an important part of data science projects. Go with the candidate who has experience in Tableau and R. They should be able to generate boxplots and scatterplots, along with heatmaps and trees.
Interview Questions
What is the goal of A/B testing?
A/B testing is a randomized test that compares 2 variables and notes their effect on the overall product. This test allows a company to collect and study data, record results, and change its current processes. Most industries use it to determine the direction their product should take.
What is supervised learning?
Supervised learning is a category of machine learning in which the algorithms are trained with labeled data.
The algorithm trains on the input data. Once sufficiently trained, the algorithm can then predict values for data outside the training dataset, i.e., new values. Supervised learning allows an algorithm to predict an output based on previously analyzed and processed data.
State differences between regression and classification
In data science, classification is the task of predicting a specific class label. The algorithm identifies the category of output for the data and classifies it into those categories. This is used for segregating data into discrete values.
Regression is the practice of speculating a continuous quantity through known data. The algorithm takes the input and generates continuous values using the best fit line. Regression problems with more than one output variable are called multivariate regression problems.
Why is Naive Bayes called naive?
Naive Bayes is a practical algorithm for predictive modeling. It is called naive because it infers that each input variable is autonomous. This assumption is usually wrong and doesn’t work for real-world data, hence the tag of naive.
What do you understand about the random forest algorithm?
A random forest algorithm is a machine learning algorithm based on decision trees. A random forest model is created by combining many decision trees together through bagging.
Random forest is much more effective than decision trees for managing bulk data. It can solve overfitting issues in decision trees and generates outcomes with low bias and variance.
Job Description
We are looking for highly qualified and experienced data science professionals for designing and implementing machine learning models. They should be experienced in Python and R and should be able to handle big data through Hadoop.
The candidate must have good communication skills and should be able to work on different aspects of data science projects, i.e., data preprocessing, cleaning, ETL, modeling, data visualization, and reporting. In addition, they should be a team player and be able to collaborate with different teams for diverse projects.
Responsibilities
- Design, develop and deploy data-based systems and architecture.
- Work on data-processing pipelines.
- Develop code to build and deploy machine learning/AI models.
- Work on the project’s features and optimize classifiers.
- Perform extraction, transformation, loading for data (ETL)
- Implement data science use-cases on Hadoop
- Work on data cleaning and standardization.
- Work on deep learning models and algorithms such as CNN and RNN.
- Work in collaboration with different stakeholders.
- Solve bugs and apply maintenance.
- Follow best industry practices and standards
- {{Add other relevant responsibilities}}
Skills and Qualifications
- Knowledge of data science toolkits, such as Scikit-learn, R, Pandas, NumPy, Matplotlib.
- Prior experience in writing and executing complex queries in SQL
- Deep understanding of machine learning techniques and algorithms such as classification, regression, random forest, decision trees.
- Experience with code versioning and collaboration tools.
- High proficiency in Python/Java/C++.
- Candidates with experience in data visualization are preferred.
- Knowledge of the big data tools (Spark, Flume) is a plus.
- {{Add other frameworks or libraries related to your development stack}}
- {{List education level or certification required}}
Conclusion
Data science plays a pivotal role in today’s industry and is on the fast rise. Many sectors such as telecommunications, health, retail, e-commerce, automotive, and digital marketing use data science to improve their services. As a business owner, it makes sense to invest in data science for your decision-making process. It enhances risk management and improves accountability to a great extent.
