ML Essentials: Top 10 lists every Data Scientist should know

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Data Science is no doubt the “sexiest” career path of the 21st century, made up of people with strong intellectual curiosity and technical expertise to dig out valuable insights from humongous volumes of data. This helps firms add value by improving their productivity, unlocking insights for better decision making and profit gains, just to mention a few. The knowledge of Data Science is desirable and useful across various industries.

The journey of a Data Scientist is full of twists and turns that will mold you. However, it is not these twists and turns that molds, rather how you handle the ones thrown at you. Many of these challenges can be prevented or minimized by having a foreknowledge of the right tool kits before kickstarting the journey or maneuvering your way in the journey of being a successful data scientist.

This article provides you with this key information needed so you can spend your time efficiently and navigate a data science career path smartly. Hence, a guide to help you find your way through the Data Science maze.

Top ✔️ 10 Websites for Data Science

  1. Analytics Vidhya
  2. Kaggle
  3. Coursera
  4. Udacity
  5. Datacamp
  6. EdX
  7. Udemy
  8. KDNuggets
  9. R-bloggers
  10. Khan Academy

Top ✔️ 10 Skills for Data Science

  1. Probability & Statistics
  2. Linear Algebra
  3. Python
  4. R
  5. SQL
  6. Tableau/Power BI
  7. AWS/Azure
  8. Spark
  9. Excel
  10. DevOps

Top ✔️ 10 Algorithms for Data Science

  1. Linear Regression
  2. Logistics Regression
  3. K-means Clustering
  4. PCA
  5. Support Vector Machine
  6. Decision Tree
  7. Random Forest
  8. Gradient Boosting Machines
  9. Naïve Bayes Classifier
  10. Artificial Neural Networks

Top ✔️ 10 Data Science Roles

  1. Data Scientist
  2. Decision Maker
  3. Analyst
  4. ETL Engineer
  5. Machine Learning Engineer
  6. Data Engineer
  7. Analytics Manager
  8. Tableau Developer
  9. Researcher
  10. BI Analyst

Top ✔️ 10 Data Science Experts to follow on LinkedIn

  1. Bernard Marr
  2. DJ Patil
  3. Francesca Lazzeri, PhD
  4. Carla Gentry
  5. Dennis R. Mortensen
  6. Andrew Ng
  7. Gregory Piatetsky-Shapiro
  8. Tom Davenport
  9. Randy Lao ️
  10. NABIH IBRAHIM BAWAZIR

Top ✔️ 10 Python Libraries for Data Science

  1. Pandas
  2. Numpy
  3. Scikit-Learn
  4. Keras
  5. PyTorch
  6. LightGBM
  7. Matplotlib
  8. SciPy
  9. Theano
  10. TensorFlow

Top ✔️ 10 Industries for Data Science

  1. Technology
  2. Finance
  3. Retail
  4. Telecom
  5. Healthcare & Pharma
  6. Manufacturing
  7. Automotive
  8. Cybersecurity
  9. Energy
  10. Utilities

Top ✔️ 10 Data Science-related Hashtags to follow on LinkedIn and to also use for engaging posts

  1. #innovation
  2. #technology
  3. #bigdata
  4. #businessintelligence
  5. #analytics
  6. #datamining
  7. #data
  8. #artificialintelligence
  9. #machinelearning
  10. #datascience

Top ✔️ 10 Data Science groups to join on LinkedIn

  1. Big Data and Analytics
  2. Advanced Analytics and Data Science
  3. Big Data, Analytics, Business Intelligence & Visualization Experts Community
  4. Data Science, Big Data, Machine Learning, Artificial Intelligence Professionals | DataScience.US
  5. Data Mining, Statistics, Big Data, Data Visualization, and Data Science
  6. Research Methods and Data Science
  7. Big Data, Analytics, IoT (Internet of Things) & Blockchain
  8. Big Data|Artificial Intelligence|Machine Learning|Predictive Analytics|Data Mining|Data Science
  9. IBM Big Data and Analytics
  10. Advanced Analytics, Predictive Modeling & Statistical Analyses Professionals Group

Top ✔️ 10 Free Dataset sources for Data Science project

  1. Kaggle
  2. UCI Machine Learning Repository
  3. Google Custom Dataset Search
  4. gov
  5. Reddit
  6. Quandl
  7. VisualData
  8. GitHub
  9. world
  10. Google Cloud Public Datasets

Good luck on your journey to becoming a top-notch Data Science expert that you desire to become. Nothing is impossible, believe it!

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This post was originally published by at Hacker Noon

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