Silicon Valley Job Skilling & Acquisition Guide

Concrete Goal Determination 1 Technical Skills and How to Learn Them 1 Resources & Curriculum Design 1 Topics List 2 Interview Opportunities and How to Access Them 5 Portfolio Construction 5 Passing Technical Interviews 6 Passing Non-Technical Interviews 6 Crafting a Story 6 Negotiating Offers 6 FAQ 7

Concrete Goal Determination This makes decision making decisive and narrows the curriculum and social outreach. You can tell people who may refer you to worthwhile positions exactly what you’re looking for, and you can build skills for that position much more efficiently. Technical Skills and How to Learn Them Resources & Curriculum Design

This will cover the path of a traditional data scientist.

Curriculum by topic:

Python Programming
Data Structures and Algorithms
Basic Machine Learning & Statistics
Machine Learning Library Creation

Curriculum by resource:

Learn Python the Hard Way
1. Incredibly Incremental Approach to Learning Python for the Beginning Programmer
Python for Data Analysis
Interactive Python (Problem Solving With Data Structures and Algorithms Using Python)
1. Data Structures Practice
Cracking the Coding Interview
1. Solve all the problems.
Some of Introduction to Algorithms (CLRS)
Hackerrank / Leetcode
1. 100 problems.
Applied Predictive Modeling
1. Accessible High Level Data Analysis / Machine Learning Textbook
Elements of Statistical Learning
Deep Learning Textbook Topics List
Data Structures
1. Hash Table
2. Linked List
3. Graphs
  1. Adjacency List
  2. Adjacency Matrix
  3. Pointers and Objects
4. Heap
  1. Fibonacci Heaps
  2. Priority Queue
5. Binary Tree
6. Binary Search Tree
  1. Balanced Binary Search Tree
    1. Red Black Trees
    2. AVL Trees
7. Queue
8. Stack
9. Dequeue
10. Arrays
11. Disjoint Set
Algorithms (Non-ML)
1. Graph Algorithms
  1. Shortest Path
    1. Dijkstra
    2. Floyd-Warshall
    3. Bellman-Ford
    4. A*
  2. Search
    1. BFS
    2. DFS
      1. Topological Sort
      2. Strongly Connected Components
  3. Minimum spanning Tree
    1. Kruskal
    2. Prim
  4. Min-Cut / Max-Flow
    1. Ford-Fulkerson
    2. Maximum bipartite matching
2. Recursion
  1. Fibonacci
3. Dynamic Programming / Recursion
  1. Knapsack
  2. Traveling Salesman Problem
  3. Longest Common Subsequence
  4. Rod Cutting
  5. Matrix-chain multiplication
  6. Optimal Binary Search Trees
4. Divide and Conquer
  1. Maximum-subarray
  2. Strassen's Algorithm
5. Greedy
  1. Huffman Codes
  2. Matroids
    1. Task-scheduling
6. Sorting
  1. O(n log(n))
    1. MergeSort
    2. Quicksort
    3. Heapsort
  2. O(n)
    1. Radix Sort
    2. Bucket Sort
  3. O(n2)
    1. Insertion Sort
    2. Selection Sort
    3. Bubble Sort
    4. Shell Sort
7. Multithreaded
  1. Multithreaded Matrix Multiplication
  2. Multithreaded MergeSort
8. Linear Programming
  1. Simplex
  2. Branch, Bound and Cut
9. Fast Fourier Transform
10. String Matching
11. Rabin-Karp
12. Knuth-Morris-Pratt
13. NP Completeness
Testing
Programming Languages
1. Functions
  1. Passing arguments by value / reference
  2. Main: Handling command-line options
  3. Return types and the return statement
  4. Overloading (Differences in the input parameters determine the function called)
  5. Polymorphism (Different behavior depending on class / type)
  6. Default Arguments
2. Types
3. Variables
  1. Val vs. Var
4. Expressions
  1. Order of Evaluation
  2. Logical and Relational Operators
  3. Assignment
  4. Increment / Decrement Operators
  5. Conditionals
  6. Type Conversions
  7. Implicit / Explicit Conversions
5. Scope
6. Constants
7. Pointers, Arrays, References
8. Compilation
9. Namespaces
10. Error Handling
11. Regular Expressions
12. Iterators
13. Predicates
14. Resource Management
15. Garbage Collection vs. Reference Counting
Concurrency
1. Tasks and Threads
2. Passing Arguments
3. Sharing Data
4. Waiting for Events
5. Communication Tasks
Object Oriented Programming
1. Classes (c++)
  1. Concrete Types
  2. Abstract Types
  3. Virtual Functions (Polymorphism)
  4. Class Hierarchies
  5. Copy and Move
  6. Constructor
2. Objects (Instances of classes, determining their type)
3. Mixins
4. Inheritance
5. Data structure framing of programming rather than logic / action based framing
6. Immutable State
Distributed Computing
1. Map-Reduce
2. In-Memory Compute
3. How to parallelize algorithms
Memory Workings & Optimization
1. Pointers
2. Bits
  1. Bit Manipulation
System Design
Data Access & Management
SQL

Interview Opportunities and How to Access Them

Portfolio Construction

Passing Technical Interviews

Typical Interview Set:

Recruiter Screen
Programming Phone Screen
Take-home assignment (Occational)

Onsite:

Programming Interview
Data Structures & Algorithms Interview

Position Specific:

Machine Learning Interview
Statistics Interview
System Design Interview
Database and Querying Interview Passing Non-Technical Interviews Crafting a Story

Take your pathway, and ask how it demonstrates:

Collaboration
1. The people you worked with and how you worked together.
Persistence / Determination
1. Describe the challenges that you faced and how you overcame them.
Focus

This is an opportunity to demonstrate clarity of thought & presentation as well as quality decision making. Mostly, this doesn’t matter. But have a stock story (<2m) to tell.

FAQ

Representing a background with little software experience?

Source: Original Google Doc