6 STEPS TO KICK OFF YOUR DATA SCIENCE LEARNING PATH IN 2021

There are many resources out there to assist you to learn data science. Considering that the demand for learning data science is high, there are various data science courses, books, libraries to help novices enter the field. Since DATA SCIENCE has gained great popularity, it has helped organizations and businesses to make better decisions by analyzing and processing huge or quite large data sets.

This has given their customers a better user experience. As you know, data is continuously generated and transmitted through various online learning platforms, thus paving the way for data scientists to efficiently complete their work and help businesses grow. Therefore, this fascinating field has indeed gained enough momentum in recent years, covering almost multiple fields such as medical care, insurance, education, banking and finance professionals, marketing managers, and supply chains.

What are the 6 steps to start learning Data Science?

Step 1: Build a foundation in math & statistics

You can build the Foundation for your Data Science career by getting hands-on experience with Python, Jupyter, SQL and so on… Moreover, the Data Science Fundamentals Specialization from IBM will assist you in pursuing your career in data science by teaching essential abilities to get begun in this in-demand field.
 
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  • None Pre-requisite
  • Big data Data Analysis Data Science Data Science with 'Python' Deep learning Machine learning Practical Statistics Probability TensorFlow
The score is based on the user experience, rated by the learners
Learning Experience
9
PROS:
  • Jam-packed with a good balance of technical and practical information.
  • Extremely eye-catching design and presentation. The way content is delivered on the screen is very attractive.
  • The exercises are very informative, exhaustive and detailed.
  • One can commence without any prior programming experience.
  • Realistic approach, balanced theories and practice.
CONS:
  • Need to expand details on other classifiers such as knn, SVMs and decision trees.
  • Need to add basic mathematics calculus.
  • Lengthy content on topics such as probability and stats.

Step 2: Learn to code

This is the IBM Data Science with Python Basics course  that will provide a beginner-friendly introduction to Python for Data Science, where you can:

  • Practice through lab workouts, and produce your very first Python scripts by yourself.
  • Start discovering Python for data science, as well as programming in general with an introduction to Python.
  • Go from no to hero in Python programs in just a matter of hours. This will give you a taste of how to start working with information in Python.
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  • Data Science Data Science with 'Python'

Step 3: Learn Machine Learning concepts and algorithms

  • In this course, you will be able to construct artificial neural networks (ANN) with the Tensor flow and Keras.
  • Categorizing of images, data, and sentiments using the deep learning
    Make forecasts utilizing linear regression, polynomial reg., and multivariate regression.
  • Visualization of data with MatPlotLib and Seaborn.
  • Implement ML at an enormous scale with Apache Spark’s MLLib.
  • Reinforcement learning– and how to construct a Pac-Man bot.
  • Categorizing data using K-Means clustering, SVM, KNN, Naive Bayes, Decision Trees, and PCA.
  • Use train/test sets and K-Fold cross-validation to select and tune your models.
  • Develop a movie recommending system using the item and user-based collective filtering.
  • Clean your input data to eliminate outliers.
  • Design and assess A/B tests utilizing T-Tests and P-Values.

 

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  • Basic Scripting in Python High School Level Maths Previous Programming Experience
  • Apache Spark Training Big data Data Mining Data Science Data Science with 'Python' Deep learning Machine learning Natural language processing Practical Statistics Probability
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Learning Experience
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PROS:
  • Commencement from basic maths.
  • Detail implementation to a neural network.
  • Very informative lessons on areas like Deep Learning & Neural networks.
CONS:
  • This course needs to have in-depth exercises.
  • Some DL topics needed better and more in depth explanation of the concepts.

Step 4: Start with Dl, NLP, Computer vision & Reinforcement learning

The natural language processing (NLP) in the python course focuses on “how to develop and understand”, and not just “how to use it”. After reading some documentation, anyone can learn to use an API in just 15 minutes. It’s not about “remembering realities”, it’s just about “seeing for yourself” through experimentation. Through this course, you will be able to visualize what is happening in the model internally. Moreover, if you want more than simply an overview then take a look at machine learning models, this course is for you.

All of the materials required for natural language processing in the python course can be downloaded and installed totally free. They will do most of their work in Numpy, Theano, and Matplotlib, and always will be available to address your concerns and assist you along your data science journey.

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  • Basic Scripting in Python Neural Network Basics TensorFlow Basics
  • Building AI Chatbots Data Science with 'Python' Deep learning Natural language processing TensorFlow
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Learning Experience
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PROS:
  • Awesome tutorial on word embedding (word2vec and glove) techniques.
  • You will find it more advanced than any other course platforms.
  • Focus on demonstration of core parts of algorithm’s.
CONS:
  • Lengthy preliminary content.
  • It should have detail practical information.

Step 5: Connect, learn and grow with community

The requirements of data science demand an extremely flexible yet versatile programming language that is easy to write or code but can handle extremely intricate mathematical processing. Here, Python comes into the picture, as it is the well-established programs language for general computing along with scientific computing. It is being constantly upgraded and has varieties of libraries as per their programming requirements. We will discuss below such functions of python which makes it the most preferred language for data science.

  • It is easy and simple, we can obtain results with fewer lines of code.
  • It’s simple and robust to handle complicated scenarios with minimum code with much less confusion.
  • It supports cross-platform, hence the same code usually works with numerous environments.
  • It executes faster than that of other similar languages like R and MATLAB.
  • Its outstanding memory management capability, especially garbage collection makes it versatile in gracefully handling a huge volume of data transformation.
  • Python has got a huge collection of libraries and varieties of packages, which assists straightway to use codes from other languages like Java or C.
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  • Apache Spark Training Data Science with 'Python' Deep learning Keras Machine learning Natural language processing TensorFlow
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Learning Experience
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PROS:
  • Great course which covers most of the important concepts of python in Machine learning.
  • It is easy to understand for people that know python & machine learning algorithms.
  • Helpful exercises which helps to get a real world understanding of applying the concepts.
CONS:
  • Some ML topics needed better and more in depth explanation of the concepts.
  • Support vector machines, DL & Big Data topics should have more information.

Step 6: Start operating at the scale of BIG DATA

In this Big Data Masters Program, you will end up being competent in tools and systems used by Big Data professionals. This Masters’s program consists of training on the Hadoop and Spark stack, Talend, Cassandra, and Apache Kafka system. The curriculum has been figured out by extensive research from about >5000 job descriptions across the globe.

The curriculum of the Big Data Masters Program is as follows:

1. Java Essentials for Big Data Masters– SELF PACED
2. Big Data Hadoop Certification Training for Big Data Masters– LIVE CLASS
3. Apache Spark and Scala Certification Training– LIVE CLASS
4. Apache Cassandra Certification Training for Big Data Masters– LIVE CLAS
5. Talend for Data Integration and Big information– LIVE CLASS
6. Apache Kafka Certification Training for Big Data Masters– LIVE CLASS

You can download the detailed syllabus for this program over here

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  • Apache Cassandra Training Apache Hadoop Training Apache Kafka Training Apache Spark Training Big data Data Science Machine learning Scala Talend
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Learning Experience
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PROS:
  • Well designed curriculum
  • Covers important aspects to master Big Data
CONS:
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