Data Science is a multidisplinary approach combining various scientific methods from the field of Mathematics,statistics,Machine learning(ML),Artificial intelligence(AI),Specialized programming and advanced analytics to extract actionable insights from large amounts of data.
If you would like to learn and master data science fundamentals and advanced techniques,herein is a roadmap that you can follow to achieve that in 100 days.
Day 1-10:Learn Python Basics
Python is a commonly used programming language by data scientists.In the first 10 days you should learn python basics such as syntax,variables,data types,operators,functions and loops.
Day 11-20:Data Types
On these days learn and understand all the various types and how to perform operations specific to each data type i.e strings,float,integer,boolean,list,tuples,sets and dictionaries.
Day 21-30:Statistics and Calculus
Calculus helps you understand mathematical functions for optimization and modelling in data science while statistics equips with tools to draw insights from data.For calculus,learn differential and integral calculus.In statistics learn descriptive statistics,inferential statistics,probability and statistical distributions.
Day 31-40:Basic Machine Learning
Machine learning focuses on making prediction from data learning from similar previous data through algorithms.Here,learn supervised learning,unsupervised learning and reinforcement learning.
Day 41-50:Deep learning
Deep learning is subset of Machine learning that involves the use of neural networks to solve complex problems.The key components that constitute deep learning are;Neural networks,deep neural networks,Activation functions,Convolutional neural networks and autoencoders among many other concepts.
Day 51-60:Data visualization
Data visualization refers to representing data graphically to communicate insights from complex data.In this phase,learn visualization tools like Matplotlib,Seaborn,ggplot2,Tableau and Power BI and how to plot the various plots and when to use the various plots like histograms,line plots,scatter plots,pie charts and heatmaps.
Day 61-70:Data Cleaning
Data cleaning is identifying and correcting errors in the data.In data cleaning learn how to handle missing values,duplicates,inconsistent data,outliers.
Day 71-80:Projects
In these 10 days focus on solving as many data science problems as you can.Join online forums such as Kaggle and Datacamp for data science challenges and competitions.While at it,remember to build your online data portfolio on linkedIn and Github.
Day 81-90:Revise
Revising all the concepts you have learnt along the way will help to address any areas that you are still lacking.
Day 91-100:Communication Skills
Data Scientists need effective communication skills to convey their findings to all stakeholders including non technical team members and executives.Learn key communication skills such as data storytelling,presentation skills,writing skills and visualization skills.
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