Data science is constantly evolving. So if you master the basic technical and soft skills, you can have a successful career as a data scientist and pursue advanced concepts such as deep learning and artificial intelligence.
Data science is such a broad field that includes various sub-departments such as data preparation and exploration, data representation and transformation, data visualization and presentation, predictive analysis and machine learning, etc.
It’s only natural for a beginner to ask the following question: What skills do I need to become a data scientist?
This article discusses 10 essential skills required for practicing data scientist. These skills could be divided into two categories, namely technological skills (math and statistics, coding skills, data confusing and preprocessing skills, data visualization skills, machine learning skills, and real project skills ) and soft skills (communication skills, skills for lifelong Learning, team player skills and ethical skills).
Data science is an area that is constantly evolving. However, once you master the basics of data science, you will get the necessary background to pursue advanced concepts like deep learning, artificial intelligence, etc.
This article discusses 10 essential skills for practicing data scientists.
1. Basic programming skills
Programming skills are essential in data science. With Python and R considered to be the two most popular programming languages in data science, having a basic understanding of both languages is crucial. Some organizations may only need knowledge of R or Python, not both.
Knowledge of Python
Familiarize yourself with basic programming skills in Python. Here are the most important packages that you should master:
- Learn Scikit
- (ii) Skills in R.
- a) Tidied up
- b) Dplyr
- c) Ggplot2
- d) Caret
- e) Stringr
(iii) Knowledge of other programming languages
Some organizations or industries may require knowledge of the following programming languages:
2. Mathematics and Statistics
(i) Statistics and Probability
Statistics and probability are used for feature visualization, data preprocessing, feature transformation, data imputation, dimension reduction, feature engineering, model evaluation, etc. Here are the topics that you need to be familiar with:
- a) mean
- b) median
- c) mode
- d) standard deviation / variance
- e) Correlation coefficient and covariance matrix
- f) Probability distributions (binomial, Poisson, normal)
- g) p-value
- h) MSE (mean square error)
- i) R2 score
- j) Bayes’ theorem (precision, recall, positive predictive value, negative predictive value, confusion matrix, ROC curve)
- k) A / B tests
- l) Monte Carlo simulation
(ii) Multivariable computation
Most machine learning models are based on a data set with multiple functions or predictors. Therefore, knowledge of multivariable analysis is extremely important for creating a machine learning model. Here are the topics that you need to be familiar with:
- a) Functions of several variables
- b) derivatives and gradients
- c) Step function, sigmoid function, logit function, ReLU function (Rectified Linear Unit)
- d) cost function
- e) Representation of functions
- f) Minimum and maximum values of a function
(iii) Linear algebra
Linear algebra is the most important math skill in machine learning. A data record is represented as a matrix. Linear algebra is used in data preprocessing, data transformation and model evaluation. Here are the topics that you need to be familiar with:
- a) vectors
- b) matrices
- c) Transpose a matrix
- d) The inversion of a matrix
- e) The determinant of a matrix
- f) point product
- g) eigenvalues
- h) Eigenvectors
(iv) Optimization Methods
Most machine learning algorithms perform predictive modeling by minimizing an objective function while learning the weights that must be applied to the test data in order to obtain the predicted labels. Here are the topics that you need to be familiar with:
- a) Cost function / objective function
- b) probability function
- c) error function
- d) Gradient descent algorithm and its variants (e.g. stochastic gradient descent algorithm)
3. Skills for preprocessing data
Data is key to any analysis in data science, be it inferential analysis, predictive analysis, or prescriptive analysis. The predictive power of a model depends on the quality of the data used in building the model. Data come in various forms, such as: B. text, table, image, voice or video. In most cases, data used for analysis need to be broken down, processed, and transformed to bring it into a form suitable for further analysis.
i) Data Wrangling :
The process of data wrangling is a critical step for any data scientist. Very rarely is data easily accessible for analysis in a data science project. It is more likely that the data is in a file or database, or extracted from documents such as web pages, tweets, or PDFs. When you know how to process and cleanse data, you can derive important insights from your data that would otherwise be hidden.
ii) Data preprocessing :
Knowledge of data preprocessing is very important and includes topics such as:
- a) Handling of missing data
- b) data imputation
- c) Handling of categorical data
- d) Coding of class names for classification problems
- e) Techniques for feature transformation and dimension reduction, such as B. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
4. Data visualization skills
Understand the essential components of good data visualization.
- a) Data component : An important first step in deciding how to visualize data is to know what type of data it is, e.g. B. categorical data, discrete data, continuous data, time series data, etc.
- b) Geometric component: Here you decide which type of visualization is suitable for your data, e.g. B. Scatter chart, line charts, bar charts, histograms, QQ charts, smooth densities, box plots, pair charts, heat maps, etc.
- c) Assignment component: Here you have to decide which variable should be used as your x-variable and which should be used as your y-variable. This is especially important if your data set is multidimensional with multiple functions.
- d) Scaling component : Here you decide which type of scales should be used, e.g. B. linear scale, logarithmic scale, etc.
- e) Labeling component: T. This includes things like axis labels , titles, legends, font size to be used, etc.
- f) Ethical component : This is where you want to make sure that your visualization tells the real story. You need to be aware of your actions when cleaning, summarizing, editing, and creating a data visualization, and make sure that you are not using your visualization to mislead or tamper with your audience.
5. Basic machine learning skills
Machine learning is a very important branch of data science. It is important to understand the machine learning framework: problem framing, data analysis, modeling, testing and evaluating, and model application.
Below are key machine learning algorithms that you need to be familiar with.
i) Supervised learning (continuous variable prediction)
- a) Basic regression
- b) Multi-regression analysis
- c) Regularized regression
ii) Supervised learning (discrete variable prediction)
- a) Logistic regression classifier
- b) support vector machine classifier
- c) KNN classifier (K-Nearest Neighbor)
- d) Decision tree classifier
- e) Random forest classifier
iii) Unsupervised learning
- KMeans clustering algorithm
6. Skills from real-world Capstone data science projects
Skills acquired through coursework alone will not make you a data scientist. A qualified data scientist must be able to provide evidence of successfully completing a real-world data science project that encompasses all phases of data science and the machine learning process, such as: B. Problem definition, data acquisition and analysis, modeling, model testing and model evaluation and provision of models. Real data science projects can be found below:
- Kaggle projects
- From interviews
7. Communication skills
Data scientists need to be able to communicate their ideas with other team members or with company administrators in their organizations.
Good communication skills would play a key role here in being able to convey and present very technical information to people with little or no understanding of technical concepts in data science.
Good communication skills promote an atmosphere of unity and togetherness with other team members such as data analysts, data engineers, field engineers, etc.
8. Be a lifelong learner
Data science is an area that is constantly evolving. So be ready to embrace and learn new technologies. One way to keep in touch with developments in this area is to network with other data scientists.
Some platforms that promote networking are LinkedIn, GitHub, and Medium ( Towards Data Science and Towards AI Publications). The platforms are very useful for keeping up to date with the latest developments in the field.
9. Team player skills
As a data scientist, you will work in a team of data analysts, engineers and administrators. Hence, you need good communication skills.
You also need to be a good listener, especially in the early stages of project development when you need to rely on engineers or other staff to help you design and craft a good data science project.
Being a good team player helps you succeed in a business environment and maintain good relationships with other members of your team, as well as administrators or directors of your organization.
10. Ethical skills in data science
Understand the impact of your project. Be honest with yourself. Avoid tampering with data or using any method that intentionally skews the results.
Be ethical at all stages from data collection and analysis to modeling, analysis, testing and application.
Avoid fabricating results in order to mislead or manipulate your audience. Be ethical in how you interpret the results of your data science project.
In summary, we discussed 10 essential skills required for practicing data scientist. Data science is an area that is constantly evolving.
However, once you master the basics of data science, you will get the background necessary to pursue advanced concepts like deep learning, artificial intelligence, etc.