WHAT IS DATA SCIENCE?
Data science is the science of analysing raw data using statistics and machine learning techniques with the purpose of drawing conclusions about that information.
If you want to learn Data Science for your college Academic projects, then it’s enough to just learn the beginner things in Data Science. Similarly, if you want to build your long-term career then you should learn professional or advanced things also. You must cover all the prerequisite things in detail. So, it’s on your hand and it’s your decision why you want to learn Data Science.
How to Learn Data Science?
Usually, data scientists come from various educational and work experience backgrounds, most should be proficient in, or in an ideal case be masters in four key areas.
Domain Knowledge
Math Skills
Computer Science
Communication Skill
Domain Knowledge:
Most people thinking that domain knowledge is not important in data science, but it is very important. Let us take an example:
If you want to be a data scientist in the banking sector, and you have much more information about the banking sector like stock trading, know about finance, etc. so this is going to be very beneficial for you and the bank itself will give more preference to these types of applicants more than a normal applicant.
Math Skills:
Linear Algebra, Multivariable Calculus & Optimization Technique, these three things are very important as they help us in understanding various machine learning algorithms that play an important role in Data Science. Similarly, understanding Statistics is very significant as this is a part of Data analysis. Probability is also significant to statistics, and it is considered a prerequisite for mastering machine learning.
Computer Science:
There is much more to learn in computer science. But when it comes to the programming language one of the major questions that arise is:
Python or R for Data Science?
There are various reasons to choose which language for Data Science as both have a rich set of libraries to implement the complex machine learning algorithm, visualization, data cleaning. Please refer to R vs Python in Data Science to know more about this.
But my recommendation is one must have knowledge of the programming language to become a successful data scientist.
Apart from the programming language the other computer science skills you must learn are:
Basics of Data Structure and Algorithm
SQL
MongoDB
Linux
Git
Distributed Computing
Machine Learning and Deep Learning, etc.
Communication Skill:
It includes both written and verbal communication. What happens in a data science project is after drawing conclusions from the analysis, the project has to be communicated to others. Sometimes this may be a report you send to your boss or team at work. Other times it may be a blog post. Often it may be a presentation to a group of colleagues. Regardless, a data science project always involves some form of communication of the projects’ findings. So, it’s necessary to have communication skills for becoming a data scientist.
Learning Resources
There are plenty of resources and videos available online and it’s confusing for someone where to start learning all the concepts. Initially, as a beginner, if you get overwhelmed with so many concepts then don’t be afraid and stop learning. Have patience, explore, and stay committed to it.
Make yourself self-motivated to learn Data Science and build some awesome projects on Data Science. Do it regularly and start learning one by one new concept on Data Science. It will be very better to join some workshops or conferences on Data Science before you start your journey. Make your goal clear and move on toward your goal.
1) Mathematics
Math skill is very important as they help us in understanding various machine learning algorithms that play an important role in Data Science.
Part 1:
Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Part 2:
Regression
Dimensionality Reduction
Density Estimation
Classification
2) Probability
Probability is also significant to statistics, and it is considered a prerequisite for mastering machine learning.
Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Binomial (⦁ Python | ⦁ R)
Bernoulli
Geometric etc
Continuous Distribution
Uniform
Exponential
Gamma
Normal Distribution (⦁ Python | ⦁ R)
3) Statistics
Understanding of Statistics is very significant as this is a part of Data analysis.
Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing (⦁ Python | ⦁ R)
ANOVA (⦁ Python | ⦁ R)
Reliability Engineering
Stochastic Process
Computer Simulation
Design of Experiments
Simple Linear Regression
Correlation
Multiple Regression (⦁ Python | ⦁ R)
Nonparametric Statistics
Sign Test
The Wilcoxon Signed-Rank Test (⦁ R)
The Wilcoxon Rank Sum Test
The Kruskal-Wallis Test (⦁ R)
Statistical Quality Control
Basics of Graphs
4) Programming
One needs to have a good grasp of programming concepts such as Data structures and Algorithms. The programming languages used are Python, R, Java, Scala. C++ is also useful in some places where performance is very important.
Python:
Python Basics
List
Set
Tuples
Dictionary
Function, etc.
NumPy
Pandas
Matplotlib /⦁ Seaborn, etc.
R:
R Basics
Vector
List
Data Frame
Matrix
Array
Function, etc.
dplyr
ggplot2
Tidyr
Shiny, etc.
Database:
SQL
MongoDB
Other:
Data Structure
Time Complexity
Web Scraping (⦁ Python | ⦁ R)
Linux
Git
SQL
5) Machine Learning
ML is one of the most vital parts of data science and the hottest subject of research among researchers so each year new advancements are made in this. One at least needs to understand basic algorithms of Supervised and Unsupervised Learning. There are multiple libraries available in Python and R for implementing these algorithms.
Introduction:
How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting ⦁ &⦁ Overfitting
Random Forests (⦁ Python | ⦁ R)
scikit-learn
Intermediate:
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation (⦁ R)
XGBoost (⦁ Python | ⦁ R)
Data Leakage
6) Deep Learning
Deep Learning uses TensorFlow and Keras to build and train neural networks for structured data.
Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification
7) Feature Engineering
In Feature Engineering discover the most effective way to improve your models.
Baseline Model
Categorical Encodings
Feature Generation
Feature Selection
8) Natural Language Processing
In NLP distinguish yourself by learning to work with text data.
Text Classification
Word Vectors
9) Data Visualization Tools
Make great data visualizations. A great way to see the power of coding!
Excel VBA
BI (Business Intelligence):
1. Tableau
2. Power BI
Qlik View
Qlik Sense
10) Deployment
The last part is doing the deployment. Whether you are fresher or 5+ years of experience, or 10+ years of experience, deployment is necessary. Because deployment will give you a fact is that you worked a lot.
Microsoft Azure
Heroku
Google Cloud Platform
Flask
DJango
11) Other Points to Learn
Domain Knowledge
Communication Skill
Reinforcement Learning
Different Case Studies:
Data Science at Netflix
Data Science at Flipkart
Project on Credit Card Fraud Detection
Project on Movie Recommendation, etc.
12) Keep Practicing
“Practice makes a man perfect” which tells the importance of continuous practice in any subject to learn anything.
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DISCLAIMER
The information is provided by Tecquisition for general informational and educational purposes only and is not a substitute for professional legal advice. If you have any feedback, comments, requests for technical support or other inquiries, please mail us by tecqusition@gmail.com.
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