Most of the courses are free in audit mode because I'd completed 55% of overall courses which is given below. I'd started my online MOOC(Massive Online Open Courses) in Nov 2019. You can check me here whatever courses I did. https://www.youracclaim.com/users/vikrant-singh.03191143

So I'm providing a complete analysis of all the best online platforms, educational blogs, tools you can use if you want to join the course, or want to build your own online learning platform.

1.Data Science Track Month 1 - Data Analysis Week 1 - Learn Python EdX https://www.edx.org/professional-certificate/python-data-science https://www.edx.org/xseries/mitx-computational-thinking-using-python

Week 2 - Statistics & Probability KhanAcademy https://www.khanacademy.org/math/statistics-probability Week 3 Data Pre-processing, Data Visualization, Exploratory Data Analysis EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis Week 4 Kaggle Project #1 Try your best at a competition of your choice from Kaggle. Use Kaggle Learn as a helpful guide

Month 2 - Machine Learning The math of Machine Learning Cheat Sheets Statistics Probability Calculus Linear Algebra Week 1-2 - Algorithms & Machine Learning Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/ Week 3 - Deep Learning Part 1 and 2 of DL Book https://www.deeplearningbook.org/ https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 Week 4 - Kaggle Project #2 Try your best at a competition of your choice from Kaggle. Make sure to add great documentation to your GitHub repository! Github is the new resume.

Month 3 - Real-World Tools Week 1 Databases (SQL + NoSQL) Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197 EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2 Week 2 Hadoop & Map-Reduce + Spark Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617 Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf Week 3 Data Storytelling Edx https://www.edx.org/course/analytics-storytelling-impact-1 Week 4 Kaggle Project #3 Try your best at a competition of your choice from Kaggle.

2.Machine Learning Track Month 1 Week 1 Linear Algebra https://www.youtube.com/watch?v=kjBOesZCoqc&index=1&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/

Week 2 Calculus https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr

Week 3 Probability https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2

Week 4 Algorithms https://www.edx.org/course/algorithm-design-analysis-pennx-sd3x

Month 2 Week 1 Learn python for data science https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU

Math of Intelligence https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D

Intro to Tensorflow https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

Week 2 Intro to ML (Udacity) https://eu.udacity.com/course/intro-to-machine-learning--ud120

Week 3-4 ML Project Ideas https://github.com/NirantK/awesome-project-ideas

Month 3 (Deep Learning) Week 1 Intro to Deep Learning https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

Week 2 Deep Learning by Fast.AI http://course.fast.ai/

Week 3-4 Re-implement DL projects from Github https://github.com/llSourcell?tab=repositories

3.Deep Learning Track https://drive.google.com/file/d/1DXdl4iPzYy7GEFRUROUv8cZRSxgUmu1E/view?usp=drivesdk

This folder contains all deep learning & Computer Science Track. It contains links to Machine Learning & Data Science Courses, books, Practice Papers, Interview, Videos, Jupyter Notebooks of many projects everything you need to know. All links connect your best Medium blogs, Youtube, Top universities free courses. We are really thankful to all contributors. This is the link for interview practices https://drive.google.com/file/d/1CL7Blkfelpcj3snyARvRXKVX6KDysLQs/view?usp=drivesdk

Booklists provided by MIT (most of them are free) https://drive.google.com/file/d/1XRCbtNz2k-H5b_CXO-ZSAxnoD6S2NZTF/view?usp=drivesdk

Data Science Books(Probability, Linear Algebra, Statistics, Data Analytics….) https://mega.nz/folder/0iZFXCbA#Rwh3Km42_YaRvgY_NOAvWw https://mega.nz/folder/g2BRhaDJ#v2XWSegTk3sH6ZcLPNG-WA

Python & Machine Learning books(Programming, Applied Statistics with R…Etc) https://mega.nz/folder/NmQRlaBa#0FKTDkkHYBmkSmcEu0kGoQ

If you want any other book & you don't want to purchase then please share the cover image of the book I will try to send a link to the complete pdf. I will upload the link of some of them in this blog in the future.

Complete Guide & Course of Quantum Machine Learning If you are interested in Physics & Philosophy then you will get ultimate links of teachers, videos, courses, companies (IBM, Google, Microsoft..Etc) developments, quantum machine learning codes(Actually all are in the developing phase but you can start with Q# or q sharp with developing some understanding in Complex Numbers)…From here https://drive.google.com/file/d/1Dy2oEsWazYlvKuqDPjEDh-79ywm_hiJX/view?usp=drivesdk

If you had just joined the kaggle If you are starting from zero, you will get everything in my previous post.

1. Everything needs to know before Data Science https://www.kaggle.com/getting-started/191220 2.Machine Learning basics https://www.kaggle.com/getting-started/191390 3.Titanic Survival Project Solution for new learners https://www.kaggle.com/vik2012kvs/titanic-survivals-project
2. Built a Chatbot in 9 Lines https://www.kaggle.com/getting-started/191218
3. Built a face Recognition app in 9 Lines https://www.kaggle.com/vik2012kvs/tutorial-face-recogination-in-9-lines 6.Interview Questions https://www.kaggle.com/questions-and-answers/191039 I wish you will get some confidence after going through the above 6 links

Dependencies of Analysis 1.Alexa Ranking 2.300+ online Learning & Tech websites(Supporting Students for searching courses) 3.Rank #1 MOOC searching Engine ClassCentral(Dhawal Shah) 4.Millions of student reviews, enrollments, ratings, etc. 5.Support, length of courses, price, course materials, etc. 6.Pros & Cons

Top Online Learning Platforms This pdf contains all trending & demanding Learning platforms. Here you get pros & cons and reviews which give you an idea before taking any course https://drive.google.com/file/d/1J0ct16O9ULpqgrmEVazhiuHGlSy2aQmI/view?usp=sharing you can reach the learning platform by just clinking the links mentioned in the text to explore & more.

Top Online Learning Blogs This is based on Alexa ranking, Number of Followers, Likes, Ratings..etc. https://drive.google.com/file/d/1J5NTL7bKW9vkx-S07CqVE2A1Dulc4gTM/view?usp=sharing From this text, you directly reach the live ranking, followers, rating, contact, email updates..etc. It is updated whenever you reach any platform via given links.

5000+Online Courses It is highly based on student's reviews who had taken the courses or going through courses. It is based on the #1 MOOC search engine ClassCentral.It is updated every day according to any change occurs in any platform in any course curriculums. Number of Courses 1.Computer Science &bArtificial Intelligence - 1928 2.Data Science- 712 3.Programming - 1425 4.Mathmatics- 517 5.Bussiness - 3313 6.Science - 1616 and many more but it covers EdX,Coursera,Future Learn,MIT MOOC,Stanfords,Harvard Extension,IBM,Google,Microsoft,NPTEL,Udacity,Udemy,…etc(premium courses including online degrees). https://drive.google.com/file/d/1JIw108xNUwdqv3CS3-FcxZmI9EwQ8Um0/view?usp=sharing

Online Platform Tools If you are looking for making your own online academy then you may get from this text. It contains the top 10 authoring tools. It contains pros, cons, price, ease of doing, etc. https://drive.google.com/file/d/1J5NTL7bKW9vkx-S07CqVE2A1Dulc4gTM/view?usp=sharing

Recommended Sites to Learn Data Science Coursera Online Learning Coursera is an online learning platform that offers courses and degrees in a variety of areas, including machine learning. It works with universities to offer more than 2,000 courses. Their courses topics include:

(i)Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

Visit: https://www.coursera.org/learn/machine-learning

Udacity Courses on Machine Learning Udacity is a for-profit educational organization that offers Massive Open Online Courses online (MOOCs). Learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then, move on to exploring deep and unsupervised learning. At each step, get practical experience by applying your skills to code exercises and projects.

Visit: https://www.udacity.com/course/intro-to-machine-learning-nanodegree–nd229

DataQuest Dataquest.io provides courses on Python, R, SQL, data visualization, data analysis, and machine learning. Visit: https://www.dataquest.io/

Data Science Masters They have collected many open-source materials online and have put together lists to learn Data Science, Math, Data Analysis, Python, and many more. Visit: http://datasciencemasters.org/

Galvanize The immersive data science curriculum includes a dive into machine learning and working on real problems in classification, regression, and clustering by utilizing structured and unstructured data sets. Students discover libraries like sci-kit-learn, NumPy, and SciPy, and use real-world case studies to root understanding of these libraries to real-world applications. Visit: https://www.galvanize.com/data-science

edX courses This course is provided by Microsoft and forms part of their Professional Program Certificate in Data Science, although it can also be taken as a stand-alone course through EdX. Students are expected to have an “introductory” knowledge of R or Python – the two most popular languages for data science programming at the moment. Subjects covered include probability and statistics, data exploration, visualization, and an introduction to machine learning, using the Microsoft Azure framework. Although all of the course material is free, students can pay ($90 in this case) for an official certificate on completion. Visit: https://www.edx.org/course/machine-learning-for-data-science-and-analytics

MIT OpenCourseWare MIT has set up a site that includes all of its courses. It is offered at no cost to participants. Visit: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/index.htm

Google Research Blog Google researchers publish a variety of papers on topics related to machine learning and deep learning. Visit: https://ai.googleblog.com/

Medium: Inside Machine Learning This site gives you deep-dive articles on a wide range of machine learning topics. From weather predictions to robots, you can explore the top machine learning case studies and get insights from industry experts. Visit: https://medium.com/inside-machine-learning

10 CalTech – Learning from Data California Institute of Technology provides a course that focuses on machine learning and is delivered as a series of video lectures along with homework assignments and a final exam.

Visit: http://work.caltech.edu/telecourse

Kaggle Wiki The Kaggle Public Wiki is a resource for learning statistics, machine learning, and other data science concepts. It offers tutorials as well as a platform for data science competitions. Visit: https://www.kaggle.com/

KDnuggets KDnuggets is a popular site that provides a vast amount of information on analytics and a variety of information on data science. Check out the content at Visit: https://www.kdnuggets.com/about/index.html

Data Science Central Data Science Central is an online site for big data practitioners. It includes a community platform with technical forums for information exchange and technical support. Visit: https://www.datasciencecentral.com/

Cognitive Class They provide learning paths for data science beginners to maximize their potential. They have online videos and a virtual lab environment to practice online. These classes are based on an IBM community initiative. Visit: https://cognitiveclass.ai/

Data Science Weekly Keep up to date on the latest meetups in your area, or join a virtual meetup featuring data science experts and sharing. Visit: https://www.datascienceweekly.org/data-science-resources/data-science-meetups

Free Courses Data Science Learning Path from Newbie to Expert Introduction to Data Science ABOUT THIS COURSE Find out the truth about what Data Science is. Hear from real practitioners telling real stories about what it means to work in data science. This course was formerly named Data Science 101. TIME TO COMPLETE: 3 Hours

COURSE SYLLABUS Module 1 – Defining Data Science Module 2 – What do data science people do? Module 3 – Data Science in Business Module 4 – Use Cases for Data Science Module 5 -Data Science People Sign up: https://cognitiveclass.ai/courses/data-science-101/

Data Science Tools ABOUT THIS COURSE Get started with some of the most popular tools for collaborative data science, including RStudio IDE, Jupyter Notebooks, Apache Zeppelin notebooks, and IBM Watson Studio. Use the tools directly on Skills Network Labs, a cloud lab environment that brings powerful open data science tools together so you can analyze, visualize, explore, clean data, run models, and create apps. TIME TO COMPLETE:4 hours

COURSE SYLLABUS Module 1 -Introducing Skills Network Labs Module 2 -Introducing Jupyter Notebooks Module 3 – Introducing Zeppelin Notebooks Module 4 – Introducing RStudio IDE Sign up: https://cognitiveclass.ai/courses/data-science-hands-open-source-tools-2/

Data Science Methodology ABOUT THIS COURSE This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem-solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn:

The major steps involved in tackling a data science problem. The major steps involved in practicing data science, from forming a concrete business or research problem to collecting and analyzing data, building a model, and understanding the feedback after model deployment. How data scientists think! TIME TO COMPLETE:5 Hours

AUDIENCE: Data Scientists, Data Engineers, Anyone with interest in Data Science

COURSE SYLLABUS Module 1: From Problem to Approach Module 2: From Requirements to Collection Module 3: From Understanding to Preparation Module 4: From Modeling to Evaluation Module 5: From Deployment to Feedback Sign up: https://cognitiveclass.ai/courses/data-science-methodology-2/

Statistics 101 ABOUT THIS COURSE Split into five modules, this is a beginner’s course covering the fundamentals of statistics. Start with mean, mode, and median. Then learn about standard deviation using examples from basketball. Learn about probability with dice. Learn what it means to group data by categorical variables, and how you can transform your data into appropriate graphs and charts. In the final module, using an open dataset, learn whether good looking professors indeed get better teaching evaluations.

This course is taught using SPSS Statistics. No prior experience necessary.

TIME TO COMPLETE:6 Hours

AUDIENCE: Beginners in statistics

COURSE SYLLABUS Module 1 – Welcome to Statistics! Module 2 – Basic Statistics Module 3 – Summarizing data Module 4- Data Visualization Module 5 – Does Beauty Pay? Sign up: https://cognitiveclass.ai/courses/statistics-101/

Predictive Modeling Fundamentals I ABOUT THIS COURSE In this course, we will be focusing on predictive modeling fundamentals. These are the mathematical algorithms, which are used to “learn” the patterns hidden in data. Learn the crucial step in the Big Data Lifecycle: using big data to make decisions!

Possess the modeling skills needed by companies all over the world to go beyond storing big data to understanding big data Learn how to use these skills to make decisions such as cancer detection, fraud detection, customer segmentation, and predicting machine downtime. Get introduced to the data mining process and modeling techniques using one of the most popular software, IBM’s SPSS Modeler. Learn how to build models on trained data, test the model with historical data, and use qualifying models on live data or other historical untested data. Save or earn companies millions of dollars with your decisions! TIME TO COMPLETE:5 Hours

AUDIENCE: Business Analysts, Management Consultants, Data Scientists, and Tech Professionals

COURSE SYLLABUS Module 1 – Introduction to Data Mining Module 2 – The Data Mining Process Module 3 – Modeling Techniques Module 4 – Model Evaluation Module 5 – Deployment on IBM Bluemix Sign up: https://cognitiveclass.ai/courses/predictive-modeling-fundamentals/

Python for Data Science ABOUT THIS PYTHON COURSE This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours. Upon its completion, you’ll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this free course is for you.

You can start creating your own data science projects and collaborating with other data scientists using IBM Watson Studio. When you sign up, you get free access to Watson Studio. Start now and take advantage of this platform.

TIME TO COMPLETE:5 hours

AUDIENCE: Anyone interested in learning to program with Python for Data Science

COURSE SYLLABUS Module 1 – Python Basics Module 2 – Python Data Structures Module 3 – Python Programming Fundamentals Module 4 – Working with Data in Python Sign up: https://cognitiveclass.ai/courses/python-for-data-science/

Data Analysis with Python ABOUT THE COURSE Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more! You will learn how to:

Import data sets Clean and prepare data for analysis Manipulate pandas DataFrame Summarize data Build machine learning models using scikit-learn Build data pipelines TIME TO COMPLETE:8 hours

AUDIENCE: Anyone who wants to use Python to analyze data

COURSE SYLLABUS Module 1 – Importing Datasets Module 2 – Cleaning and Preparing the Data Module 3 – Summarizing the Data Frame Module 4 – Model Development Module 5 – Model Evaluation Sign up: https://cognitiveclass.ai/courses/data-analysis-python/

Data Visualization with Python ABOUT THIS DATA VISUALIZATION COURSE “A picture is worth a thousand words”. We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data, and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.

TIME TO COMPLETE:10 hours

AUDIENCE: Anyone interested in data science and has completed Python 101 and Data Analysis with Python

COURSE SYLLABUS Module 1 – Introduction to Visualization Tools Module 2 – Basic Visualization Tools Module 3 – Specialized Visualization Tools Module 4 – Advanced Visualization Tools Module 5 – Creating Maps and Visualizing Geospatial Data Sign up: https://cognitiveclass.ai/courses/data-visualization-with-python/

Machine Learning with Python ABOUT THIS COURSE This Machine Learning with Python course dives into the basics of Machine Learning using Python, an approachable and well-known programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine Learning and how it affects society in ways you may not have guessed! Explore many algorithms and models:

Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. More important, you will transform your theoretical knowledge into practical skills using many hands-on labs.

TIME TO COMPLETE:12 Hours

AUDIENCE: Anyone interested in Machine Learning and Python

COURSE SYLLABUS Module 1 – Introduction to Machine Learning Module 2 – Regression Module 3 – Classification Module 4 – Unsupervised Learning Module 5 – Recommender Systems Sign up: https://cognitiveclass.ai/courses/machine-learning-with-python/

Deep Learning Fundamentals ABOUT THIS COURSE Get a crash course on what there is to learn and how to go about learning more. Deep Learning presents a simplified explanation of some of the hottest topics in data science today: What is Deep Learning? What are convolutional neural networks? Why is deep learning so powerful and what can it be used for? Be part of a rapidly growing field in data science; there’s no better time than now to get started with neural networks. COURSE SYLLABUS Module 1 – Introduction to Deep Learning Module 2 – Deep Learning Models Module 3 – Additional Deep Learning Models Module 4 – Deep Learning Platforms and Software Libraries What is a Deep Learning Platform? H2O.ai Dato GraphLab What is a Deep Learning Library? Theano Caffe TensorFlow Sign up: https://cognitiveclass.ai/courses/introduction-deep-learning/

Deep Learning with TensorFlow ABOUT THE COURSE This Deep Learning with TensorFlow course focuses on TensorFlow. If you are new to the subject of deep learning, consider taking our Deep Learning 101 course first. TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.

In this TensorFlow course, you will be able to learn the basic concepts of TensorFlow, the main functions, operations, and the execution pipeline. Starting with a simple “Hello World” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification, and minimization of error functions. This concept is then explored in the Deep Learning world. You will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.

TIME TO COMPLETE:10 Hours

AUDIENCE: Anyone interested in Machine Learning, Deep Learning, and TensorFlow

COURSE SYLLABUS Module 1 – Introduction to TensorFlow Module 2 – Convolutional Neural Networks (CNN) Module 3 – Recurrent Neural Networks (RNN) Module 4 – Unsupervised Learning Module 5 – Autoencoders Sign up: https://cognitiveclass.ai/courses/deep-learning-tensorflow/

Scope of Data Science & Rise of Data sources

Rights of Data Science

Modern Data Scientist

Data Regulations & Data Scientist

Steps for Beginner

Responsibilities

Resume

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

Mathematics and Statistics Skills (i) Statistics and Probability

Statistics and Probability is used for visualization of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with:

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

Most machine learning models are built with a data set having several features or predictors. Hence, familiarity with multivariable calculus is extremely important for building a machine learning model. Here are the topics you need to be familiar with:

a) Functions of several variables

b) Derivatives and gradients

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

Linear algebra is the most important math skill in machine learning. A data set is represented as a matrix. Linear algebra is used in data preprocessing, data transformation, and model evaluation. Here are the topics you need to be familiar with:

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

Most machine learning algorithms perform predictive modeling by minimizing an objective function, thereby learning the weights that must be applied to the testing data to obtain the predicted labels. Here are the topics you need to be familiar with:

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.

Essential Programming Skills Programming skills are essential in data science. Since Python and R are considered the two most popular programming languages in data science, essential knowledge in both languages is crucial. Some organizations may only require skills in either R or Python, not both.

(i) Skills in Python

Be familiar with basic programming skills in python. Here are the most important packages that you should master how to use:

a) Numpy

b) Pandas

c) Matplotlib

d) Seaborn

e) Scikit-learn

f) PyTorch

(ii) Skills in R

a) Tidyverse

b) Dplyr

c) Ggplot2

d) Caret

e) Stringr

(iii) Skills in Other Programming Languages

Skills in the following programming languages may be required by some organizations or industries:

a) Excel

b) Tableau

c) Hadoop

d) SQL

e) Spark

Data Wrangling and Preprocessing Skills Data is key for 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 that was used in building the model. Data comes in different forms, such as text, table, image, voice, or video. Most often, data that is used for analysis has to be mined, processed and transformed to render it to 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 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).

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., 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, bar plots, histograms, Q-Q plots, smooth densities, boxplots, pair plots, 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 includes 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.

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, Model Building, Testing & Evaluation, and Model Application. Find out more about the machine learning framework from here: The Machine Learning Process.

The following are important machine learning algorithms 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) K-nearest neighbor (KNN) Classifier

d) Decision Tree Classifier

e) Random Forest Classifier

iii) Unsupervised Learning

a) KMeans clustering algorithm

Skills from Real World Capstone Data Science Projects Skills acquired from course work alone will not make you a data scientist. A qualified data scientist must be able to demonstrate evidence of successful completion of a real-world data science project that includes every stage in data science and machine learning process such as problem framing, data acquisition and analysis, model building, model testing, model evaluation, and deploying models. Real-world data science projects could be found in the following:

a) Kaggle Projects

b) Internships

c) From Interviews

Communication Skills Data scientists need to be able to 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.

Be a Lifelong Learner Data science is an ever-evolving field, so be prepared to embrace and learn new technologies. One way to keep in touch with developments in the field is to network with other data scientists. Some platforms that promote networking are LinkedIn, GitHub, and Medium (Towards Data Science and AI publications). The platforms are very useful for up-to-date information about recent developments in the field.

Team Player Skills As a data scientist, you will be working in a team of data analysts, engineers, administrators, so you need good communication skills. You need to be a good listener, too, especially during early project development phases where you need to rely on engineers or other personnel to be able to design and frame a good data science project. Being a good team player will help you to thrive in a business environment and maintain good relationships with other members of your team as well as administrators or directors of your organization.

Ethical Skills in Data Science Understand the implication of your project. Be truthful to yourself. Avoid manipulating data or using a method that will intentionally produce bias in results. Be ethical in all phases, from data collection and analysis to model building, analysis, testing, and application. Avoid fabricating results to mislead or manipulate your audience. Be ethical in the way you interpret the findings from your data science project.

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