• ## Influencers Are Utilizing Collaboration To Win Against AI Algorithms; Here’s How

Data Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, etc. For beginners, it’s only natural to raise the following question: What skills do I need to become 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).

Data science is a field that is ever-evolving, however mastering the foundations of data science will provide you with the necessary background that you need to pursue advance concepts such as deep learning, artificial intelligence, etc. This article will discuss 10 essential skills for practicing data scientists.

# 1. Mathematics and Statistics Skills

## (I) Statistics and Probability

a) Mean

b) Median

c) Mode

d) Standard deviation/variance

e) Correlation coefficient and the covariance matrix

f) Probability distributions (Binomial, Poisson, Normal)

g) p-value

h) MSE (mean square error)

i) R2 Score

j) Baye’s Theorem (Precision, Recall, Positive Predictive Value, Negative Predictive Value, Confusion Matrix, ROC Curve)

k) A/B Testing

l) Monte Carlo Simulation

## (II) Multivariable Calculus

a) Functions of several variables

c) Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function

d) Cost function

e) Plotting of functions

f) Minimum and Maximum values of a function

## (III) Linear Algebra

a) Vectors

b) Matrices

c) Transpose of a matrix

d) The inverse of a matrix

e) The determinant of a matrix

f) Dot product

g) Eigenvalues

h) Eigenvectors

## (IV) Optimization Methods

a) Cost function/Objective function

b) Likelihood function

c) Error function

d) Gradient Descent Algorithm and its variants (e.g. Stochastic Gradient Descent Algorithm)

Find out more about the gradient descent algorithm here: Machine Learning: How the Gradient Descent Algorithm Works.

a) Numpy

b) Pandas

c) Matplotlib

d) Seaborn

e) Scikit-learn

f) PyTorch

b) Dplyr

c) Ggplot2

d) Caret

e) Stringr

a) Excel

b) Tableau

d) SQL

e) Spark

# 3. Data Wrangling and Proprocessing Skills

i) Data Wrangling: The process of data wrangling is a critical step for any data scientist. Very rarely is data easily accessible in a data science project for analysis. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. Knowing how to wrangle and clean data will enable you to derive critical insights from your data that would otherwise be hidden.

ii) Data Preprocessing: Knowledge about data preprocessing is very important and include topics such as:

a) Dealing with missing data

b) Data imputation

c) Handling categorical data

d) Encoding class labels for classification problems

e) Techniques of feature transformation and dimensionality reduction such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).

# 4. Data Visualization Skills

a) Data Component: An important first step in deciding how to visualize data is to know what type of data it is, e.g. categorical data, discrete data, continuous data, time series data, etc.

b) Geometric Component: Here is where you decide what kind of visualization is suitable for your data, e.g. scatter plot, line graphs, barplots, histograms, qqplots, smooth densities, boxplots, pairplots, heatmaps, etc.

c) Mapping Component: Here you need to decide what variable to use as your x-variable and what to use as your y-variable. This is important especially when your dataset is multi-dimensional with several features.

d) Scale Component: Here you decide what kind of scales to use, e.g. linear scale, log scale, etc.

e) Labels Component: This include things like axes labels, titles, legends, font size to use, etc.

f) Ethical Component: Here, you want to make sure your visualization tells the true story. You need to be aware of your actions when cleaning, summarizing, manipulating and producing a data visualization and ensure you aren’t using your visualization to mislead or manipulate your audience.

# 5. Basic Machine Learning Skills

The following are important machine learning algorithms to be familiar with.

## i) Supervised Learning (Continuous Variable Prediction)

b) Multiregression analysis

c) Regularized regression

## ii) Supervised Learning (Discrete Variable Prediction)

b) Support Vector Machine Classifier

c) K-nearest neighbor (KNN) Classifier

d) Decision Tree Classifier

e) Random Forest Classifier

# 6. Skills from Real World Capstone Data Science Projects

a) Kaggle Projects

b) Internships

c) From Interviews

# 10. Ethical Skills in Data Science

In summary, we’ve discussed 10 essential skills needed for practicing data scientists. Data science is a field that is ever-evolving, however mastering the foundations of data science will provide you with the necessary background that you need to pursue advance concepts such as deep learning, artificial intelligence, etc.

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