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 1 | # Learn_Data_Science_in_3_Months
 2 | 
 3 | #### Course Objective
 4 | 
 5 | This is the Curriculum for [Learn Data Science in 3 Months](https://youtu.be/9rDhY1P3YLA) by Siraj Raval on Youtube. After completing this course, start applying for jobs, doing contract work, start your own data science consulting group, or just keep on learning. Remember to believe in your ability to learn. You can learn data science, you will learn data science, and if you stick to it, eventually you will master it. 
 6 | 
 7 | ## Find a study buddy
 8 | Join the #DataSciencein3Months channel in our [Slack](http://wizards.herokuapp.com) channel to find one. 
 9 | 
10 | #### Components
11 | - 3 Projects 
12 | - 1 Weekly assignment. Pick 1 from the course for each week, do it in a weekend. 
13 | 
14 | #### Course Length
15 | - 12 Weeks
16 | - 2-3 Hours of Study per Day
17 | 
18 | #### Tools Used
19 | - Python, SQL, R, Tensorflow, Hadoop, MapReduce, Spark, GitHub, 
20 | 
21 | ### Accelerated Learning Techniques
22 | - Watch videos at 2x or 3x speed using a browser extension
23 | - Handwrite notes as you watch for memory retention
24 | - Immerse yourself in the [community](https://medium.com/@exastax/top-20-data-science-blogs-and-websites-for-data-scientists-d88b7d99740)
25 | 
26 | # Month 1 - Data Analysis
27 | 
28 | ## Week 1 - Learn Python
29 | - EdX https://www.edx.org/course/introduction-python-data-science-2
30 | - Siraj Raval https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU 
31 | 
32 | ## Week 2 - Statistics & Probability
33 | - KhanAcademy https://www.khanacademy.org/math/statistics-probability
34 | 
35 | ## Week 3 Data Pre-processing, Data Visualization, Exploratory Data Analysis
36 | - EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis
37 | 
38 | ## Week 4 Kaggle Project #1
39 | - Try your best at a competition of your choice from [Kaggle](https://www.kaggle.com/competitions).
40 | - Use [Kaggle Learn](https://www.kaggle.com/learn/overview) as a helpful guide
41 | 
42 | 
43 | # Month 2 - Machine Learning
44 | 
45 | #### Math of Machine Learning Cheat Sheets
46 | - [Statistics](http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf)
47 | - [Probability](https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf)
48 | - [Calculus](http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf)
49 | - [Linear Algebra](https://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf)
50 | 
51 | ## Week 1-2 - Algorithms & Machine Learning
52 | - Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/
53 | 
54 | ## Week 3 - Deep Learning
55 | - Part 1 and 2 of DL Book https://www.deeplearningbook.org/ 
56 | - Siraj Raval https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 
57 | 
58 | ## Week 4 - Kaggle Project #2 
59 | - Try your best at a competition of your choice from [Kaggle](https://www.kaggle.com/competitions). Make sure to add great documentation to your github repository! Github is the new resume. 
60 | 
61 | # Month 3 - Real-World Tools
62 | 
63 | ## Week 1 Databases (SQL + NoSQL) 
64 | - Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197
65 | - EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2
66 | 
67 | ## Week 2 Hadoop & Map Reduce + Spark
68 | - Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617
69 | - Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf 
70 | 
71 | ## Week 3 Data Storytelling
72 | - Edx https://www.edx.org/course/analytics-storytelling-impact-1
73 | 
74 | ## Week 4 Kaggle Project #3
75 | - Try your best at a competition of your choice from [Kaggle](https://www.kaggle.com/competitions).
76 | 


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