10 Must have skills to become a data scientist

To become a data scientist, one should have a strong foundation in math and statistics, proficiency in at least one programming language such as Python or R, knowledge of data wrangling and preprocessing techniques, experience with data visualization tools, understanding of machine learning concepts and techniques, and experience working on real-world projects. Additionally, strong communication skills, the ability to continuously learn and adapt, team-player attitude and understanding of ethical considerations in handling data are also important for a data scientist.

This article will discuss 10 essential skills that are necessary for practicing data scientists. These skills could be grouped into 2 categories, namely, technological skills (Math & Statistics, Coding Skills, Data Wrangling & Preprocessing Skills, Data Visualization Skills, Machine Learning Skills,and Real World Project Skills) and soft skills (Communication Skills, Lifelong Learning Skills, Team Player Skills and Ethical Skills).

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1. Mathematics and Statistics Skills

(I) Statistics and Probability

Having a strong foundation in math and statistics is essential for a data scientist. Statistics and probability play a critical role in data science, as they are used in many different aspects of the data science workflow, including data visualization, data preprocessing, feature transformation, model evaluation, and more.

Familiarity with the topics you listed, such as Mean, Median, Mode, Standard deviation, Correlation coefficient, Probability distributions, P-value, MSE, R2 score, Baye's theorem, A/B testing, and Monte Carlo simulation, is important for a data scientist to have.

Additionally, it's also good to have a deeper understanding of the concepts and be able to apply them to real-world problems.

(II) Multivariable Calculus

Multivariable calculus is also important for a data scientist, particularly when it comes to building machine learning models. Many machine learning algorithms, such as gradient descent and backpropagation, are based on concepts from multivariable calculus.

Familiarity with topics such as Functions of several variables, Derivatives and gradients, Step function, Sigmoid function, Logit function, ReLU function, Cost function, Plotting of functions and Minimum and maximum values of a function is important for understanding and implementing machine learning algorithms.

Additionally, a deeper understanding of multivariable calculus will also help a data scientist to understand the underlying mechanics of the model, which will allow them to make better decisions when fine-tuning it.

(III) Linear Algebra

Linear algebra is a key mathematical tool used in data science and machine learning, as it allows for the manipulation and analysis of large sets of data in the form of matrices and vectors. In order to become a data scientist, it is important to be familiar with the following concepts in linear algebra:

a) Vectors:

mathematical objects that can represent any type of data, such as a point in space or a set of observations.

b) Matrices:

a rectangular array of numbers, symbols or expressions, arranged in rows and columns.

c) Transpose of a matrix:

operation that flips the matrix over its diagonal, resulting in the interchange of rows and columns.

d) Inverse of a matrix:

a matrix that, when multiplied by the original matrix, results in the identity matrix.

e) Determinant of a matrix:

scalar value that can be calculated from a square matrix, used in various mathematical operations.

f) Dot product:

a scalar that is the product of the magnitudes of the two vectors and the cosine of the angle between them.

g) Eigenvalues:

a scalar value that characterizes a matrix, used in various mathematical operations

h) Eigenvectors:

a non-zero vector that changes by a scalar factor when transformed by a matrix, used to understand the underlying structure of the data.

Knowing linear algebra will give you a powerful toolset to manipulate and analyze data, and to build and evaluate models in machine learning.

(IV) Optimization Methods

Optimization methods are a key component of many machine learning algorithms, as they are used to find the optimal solution to an objective function. In order to become a data scientist, it is important to be familiar with the following concepts and techniques in optimization:

a) Cost function/Objective function:

a mathematical function that measures the difference between the predicted and actual values of a model. The goal of optimization is to minimize this function.

b) Likelihood function:

a function that describes the probability of observing a set of data given a set of parameters. It is often used in maximum likelihood estimation.

c) Error function:

a function that measures the difference between the predicted and actual values of a model.

d) Gradient Descent Algorithm and its variants:

a optimization algorithm that iteratively updates the model parameters in the direction of the negative gradient of the objective function. Variants such as Stochastic Gradient Descent algorithm can be used for large data set for faster convergence and avoiding local minima.

It is also important to be familiar with other optimization techniques such as conjugate gradient, Newton-Raphson, BFGS, L-BFGS, etc.

Understanding optimization techniques is important because they allow you to find the best possible solution to a given problem, and to build more accurate and efficient models.

2. Essential Programming Skills

Programming skills are essential in data science because they allow data scientists to effectively manipulate and analyze large sets of data. Python and R are popular programming languages in data science because they have a wide range of powerful libraries and frameworks specifically designed for data manipulation and analysis, such as NumPy, pandas, and scikit-learn in Python, and dplyr, ggplot2, and caret in R.

Having knowledge in both Python and R can be beneficial because it allows data scientists to choose the best tool for the job and to easily switch between the two depending on the specific requirements of a project or the preferences of the organization they are working for. However, it's worth noting that some organizations may only require skills in one of the languages, and thus having knowledge in both may not be crucial.

(I) Skills in Python

Numpy, pandas, Matplotlib, Seaborn, scikit-learn and PyTorch are some of the most popular and widely used Python libraries in data science.

Numpy

provides powerful tools for numerical computing, including multi-dimensional arrays and matrices.

Pandas

is a powerful library for data manipulation and analysis, including data cleaning, filtering, and transformation.

Matplotlib and Seaborn

are powerful libraries for data visualization, allowing data scientists to create high-quality plots and charts to help understand and communicate their findings.

Scikit-learn

is a popular library for machine learning, providing a wide range of tools and algorithms for tasks such as classification, regression, and clustering.

PyTorch

is a popular library for deep learning, allowing data scientists to build and train neural networks for tasks such as image and language processing.

Mastering these libraries and understanding how to use them effectively is crucial for data scientists to be able to manipulate and analyze data in an efficient and accurate way.

(II) Skills in R

Tidyverse, dplyr, ggplot2, caret, and stringr are some of the most popular and widely used R libraries in data science.

Tidyverse

is a collection of R packages designed for data manipulation and visualization, including dplyr and ggplot2.

Dplyr

is a powerful library for data manipulation, providing a concise and expressive syntax for tasks such as filtering, grouping, and summarizing data.

Ggplot2

is a powerful library for data visualization, allowing data scientists to create high-quality plots and charts to help understand and communicate their findings.

Caret

is a popular library for machine learning, providing a wide range of tools and algorithms for tasks such as classification, regression, and clustering.

Stringr

is a library for string manipulation, providing functions for tasks such as pattern matching and text cleaning, which is important for text mining and natural language processing.

(III) Skills in Other Programming Languages

Skills in Excel, Tableau, Hadoop, SQL, and Spark are also important for data science, as they are used for different purposes and in different industries.

Excel

is a widely-used spreadsheet software, which is often used for data cleaning, exploration, and visualization.

Tableau

is a popular data visualization tool that allows data scientists to create interactive visualizations and dashboards.

Hadoop

is an open-source framework for storing and processing big data, which is used in industries such as finance and healthcare to analyze large sets of data.

SQL

is a programming language used to manage and manipulate relational databases. It's used to extract and manage data from databases to gain insights.

Spark

is an open-source, distributed computing system that can process large amounts of data quickly. It's often used in combination with Hadoop to analyze big data.

Having knowledge of these programming languages and tools can be beneficial for data scientists as it allows them to work with different types of data and in different industries.

3. Data Wrangling and Preprocessing Skills

Data wrangling and preprocessing are critical skills in data science, as they allow data scientists to effectively prepare and clean data for analysis.

I) Data Wrangling:

The process of data wrangling involves several steps, including:

· Data acquisition: This is the process of obtaining data from various sources, such as files, databases, or web pages.

· Data cleaning: This is the process of identifying and correcting errors or inconsistencies in the data, such as missing values, outliers, or duplicate records.

· Data integration: This is the process of merging multiple datasets into a single dataset for analysis.

· Data transformation: This is the process of converting data into a format that can be used for analysis, such as converting text to numerical values or creating new variables from existing ones.

II) Data Preprocessing:

Data preprocessing is an important step in data science, and includes several key tasks such as:

· Dealing with missing data: This involves identifying and handling missing values in the data, such as by dropping or imputing the missing values.

· Data imputation: This is the process of replacing missing values with estimated or inferred values.

· Handling categorical data: This involves converting categorical variables into numerical values, such as through one-hot encoding or label encoding.

· Encoding class labels for classification problems: This involves encoding the target variable into numerical values, so that it can be used in machine learning algorithms.

· Techniques of feature transformation and dimensionality reduction: This involves applying techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to reduce the dimensionality of the data and extract the most important features for analysis.

Data preprocessing requires a good understanding of the data, the problem and the machine learning algorithm that will be used. It's also important to have knowledge about different techniques for handling the data. Having good knowledge about data preprocessing enables data scientist to make the most out of the data and feed the model with the most important features.

4. Data Visualization Skills

Data visualization is a critical skill in data science, as it allows data scientists to effectively communicate and present their findings. Understanding the essential components of a good data visualization is crucial for creating effective and informative visualizations.

· Data component: Knowing the type of data you are working with is an important first step in deciding how to visualize it. Different types of data, such as categorical, discrete, continuous, or time series data, may require different types of visualizations.

· Geometric component: This involves deciding what type of visualization is most suitable for your data. Different visualizations, such as scatter plots, line graphs, bar plots, histograms, etc, are better suited for different types of data and can convey different types of information.

· Mapping component: When working with multi-dimensional data, it is important to decide what variable to use as the x-variable and what to use as the y-variable, as well as which variables to include in the visualization.

· Scale component: Choosing the appropriate scale for the visualization, such as linear or logarithmic, can also affect how the data is interpreted.

· Labels component: This includes adding appropriate labels, such as axes labels, titles, and legends, and choosing the right font size for the visualization.

· Ethical component: It's important to ensure that the visualization tells the true story, and not to mislead or manipulate the audience. It's important to be aware of one's actions when cleaning, summarizing, manipulating and producing a data visualization to ensure that it is accurate and fair representation of the data.

5. Basic Machine Learning Skills

Machine learning is a key component of data science, and understanding the machine learning framework is important for effectively applying machine learning techniques to solve problems.

The machine learning framework generally includes the following steps:

· Problem framing: Identifying the problem and determining how it can be solved using machine learning.

· Data analysis: Exploring and understanding the data that will be used for the model.

· Model building, testing, and evaluation: Building, testing and evaluating a model using appropriate machine learning algorithms and techniques.

· Model application: Applying the model to new data in order to make predictions or decisions.

It's also important to be familiar with a variety of machine learning algorithms. The following are some important machine learning algorithms to be familiar with:

I) Supervised Learning (Continuous Variable Prediction)

· Basic regression: A simple algorithm used to predict a continuous variable based on one or more other variables.

· Multiregression analysis: An extension of basic regression, used to predict a continuous variable based on multiple other variables.

· Regularized regression: A variation of basic regression that adds a regularization term to the cost function in order to reduce overfitting.

II) Supervised Learning (Discrete Variable Prediction)

· Logistic Regression Classifier: An algorithm used to predict a binary outcome.

· Support Vector Machine Classifier: A powerful algorithm for classification, which aims to find the boundary that maximizes the margin between different classes.

· K-nearest neighbor (KNN) Classifier: An algorithm that classifies a new observation based on the majority class of its k nearest neighbors.

· Decision Tree Classifier: An algorithm that recursively splits the data into subsets based on the values of the input features, with the goal of creating subsets that are as "pure" as possible.

· Random Forest Classifier: An ensemble method that builds multiple decision trees and combines their predictions to improve the overall accuracy of the model.

III) Unsupervised Learning

· K-means Clustering Algorithm: A popular algorithm used to group similar data points together based on their features.

It's worth noting that these are just a few examples of many machine learning algorithms available, there are many more and new ones are constantly being developed. It's important for a data scientist to have a good understanding of the main concepts of machine learning and to be able to choose the best algorithm for a specific task and problem.

6. Skills from Real World Capstone Data Science Projects

Skills acquired from coursework alone will not make you a data scientist. It's important to have hands-on experience working on real-world data science projects in order to truly understand the complexities and nuances of the field.

· Kaggle projects: Kaggle is a platform that hosts data science competitions, and working on a Kaggle project can be a great way to gain experience working on a real-world problem with a large dataset.

· Internships: Interning at a company or organization that uses data science can provide valuable experience working on real-world projects and understanding the challenges and considerations that come with applying data science in a professional setting.

· Interviews: Even if an interview may not have a real-world project, but it can give you a sense of the types of questions and problems that data scientists are expected to solve in the industry.

It's important to note that the experience from real-world projects can help you to improve your problem-solving skills, develop a deeper understanding of the data science process, and gain experience with the tools and techniques used in industry. It also makes you more attractive to potential employers, as they will be able to see evidence of your ability to apply your knowledge in a real-world setting.

7. Communication Skills

Data scientists need to be able communicate their ideas with other members of the team or with business administrators in their organizations. Good communication skills would play a key role here to be able to convey and present very technical information to people with little or no understanding of technical concepts in data science. Good communication skills will help foster 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 a constantly evolving field, so it is important for data scientists to stay current with new technologies and developments. Networking with other data scientists through platforms such as LinkedIn, GitHub, and Medium (such as the publications "Towards Data Science" and "Towards AI") can help keep you informed about recent advancements in the field.

9. Team Player Skills

Being a good team player is an important skill for a data scientist, as they will often work with a team of data analysts, engineers, and administrators. Strong communication skills and the ability to listen effectively, especially during the early stages of a project, are crucial for designing and developing a successful data science project. Being a good team player can also help to foster positive relationships with colleagues and leadership within the organization.

10. Ethical Skills in Data Science

Having ethical skills in data science is crucial in ensuring that the projects and research being conducted are fair, unbiased, and responsible. It is important to understand the implications of a project and to avoid manipulating data or using methods that will intentionally produce bias in results. Additionally, it is essential to be truthful and ethical in all phases of a project, from data collection to analysis, model building, testing, and application. It's important to avoid fabricating results for the purpose of misleading or manipulating the audience and interpret the findings from the data science project in an ethical way.

In summary

being a successful data scientist requires mastering a set of essential skills. It's important to keep in mind that data science is a constantly evolving field, but having a strong foundation in these skills will provide the necessary background to pursue advanced concepts such as deep learning and artificial intelligence.