Caleb Curry has everything you need to know to get started as a C++ Programming Software developer / Software engineer — starting off with the super basics and then to intermediate topics.

Time indexes are below the video so you can jump to the section you want.

Timestamps:
1:09 – Intro
9:31 – Installing g++
15:37 – C++ Concepts
22:31 – More C++ Concepts
30:48 – Using Directive and Declaration
37:33 – Variable Declaration and Initialization
40:40 – Using Variables with cout
44:46 – User Input with cin
47:57 – Conventions and Style Guides
56:23 – Intro to Functions
1:01:28 – Intro to Creating Custom Functions
1:09:05 – Pow Function
1:13:13 – Creating Custom Functions
1:22:20 – Creating Void Functions
1:29:11 – Intro to C++ Data Types
1:37:17 – Integral Data Types and Signed vs Unsigned
1:44:38 – Integral Data Types, sizeof, limit
1:49:35 – char Data Type
2:02:21 – bool Data Type
2:06:54 – Floating Point Numbers
2:15:00 – Constant const, macro, and enum
2:21:00 – Numeric Functions
2:28:37 – String Class and C Strings
2:37:47 – get line for Strings
2:40:52 – String Modifier Methods
2:47:56 – String Operation Methods
2:54:36 – Literals
2:59:42 – Hex and Octal
3:04:06 – Operator Precedence and Associativity
3:11:53 – Reviewing Key Concepts
3:17:54 – Control Flow
3:27:32 – If Statement Practice
3:32:33 – Logical and Comparison Operators
3:42:27 – Switch Statement and Enum
3:51:00 – Intro to Loops
3:58:01 – For Loops (How to Calculate Factorial)
4:05:06 – While Loop and Factorial Calculator
4:12:29 – Do While Loop
4:20:04 – Break and Continue
4:25:14 – Conditional Operator
4:29:04 – Intro to Our App
4:33:00 – Creating a Menu
4:38:11 – Creating a Guessing Game
4:45:27 – Intro to Arrays and Vectors
4:54:15 – Working with Arrays
5:04:21 – Passing Arrays to Functions
5:11:11 – Fill Array from Input
5:20:42 – Using and Array to Keep Track of Guessing
5:25:55 – Intro to Vectors
5:33:00 – Creating a Vector
5:36:39 – Passing Vectors to Functions
5:39:55 – Refactor Guessing Game to Use Vectors
5:43:47 – STL Array
5:47:47 – STL Arrays in Practice
5:51:45 – Refactor Guessing Game to Use Templatized Array
5:55:13 – Array vs Vector vs STL Array
5:01:49 – Range Based for Loop
6:07:03 – Intro to IO Streams
6:15:45 – Writing to Files with ofstream
6:24:43 – Readings from Files with ifstream
6:31:13 – Saving High Scores to File
6:39:46 – Functions and Constructors
6:47:53 – Refactoring IO to Function Call and Testing
6:54:31 – Multidimensional Arrays and Nested Vectors
6:59:29 – Const Modifier
7:04:33 – Pass by Reference and Pass By Value
7:11:41 – Swap Function with Pass by Reference
7:14:27 – Intro to Function Overloading
7:19:35 – Function Overloading Examples
7:26:22 – Default Arguments
7:33:24 – Intro to Multifile Compilation
7:40:56 – Multifile Compilation
7:48:15 – Makefiles
7:54:44 – Creating a Simple Makefile
8:01:30 – Intro to Namespaces
8:05:33 – Creating a Namespace
8:10:24 – Intro to Function Templates
8:15:24 – Creating a Function Template
8:18:40 – Overloading Function Templates
8:23:18 – Intro to Object Oriented Programming
8:30:39 – Intro to Structs
8:35:59 – Creating a Struct
8:41:48 – Classes and Object
8:49:58 – Creating a Class
8:52:45 – Working with Objects
9:00:11 – Intro to Constructors
9:05:18 – Constructors and Destructors
9:09:53 – Encapsulation
9:15:40 – Getters and Setters
9:22:47 – Static Data Members
9:29:59 – Intro to Operator Overloading
9:34:07 – Operator Overloading == and +
9:41:13 – Overloading Insert and Extraction Operators
9:49:07 – Friend Functions and Operator Overloading
9:56:14 – Class Across Files
10:03:31 – Inheritance and Polymorphism
10:08:49 – Base Classes and Subclasses Inheritance
10:16:54 – Polymorphism
10:23:15 – Conclusion

One of the questions that I get asked a lot is how to get started in Data Science. In some ways, it’s easier now than when I started — there’s a lot more learning resources. In others, it’s harder — there’s a lot more competition for data science jobs.

Here’s a great article from towardsdatascience.com with six bits of advice for those wanting to get into this field.

Building experience before landing a job Data science is a field with a huge demand, in part because it seems to require experience as a data scientist to be hired as a data scientist. But many of the best data scientists I’ve worked with have diverse backgrounds ranging from

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If you’re curious about getting into Data Science, then here’s a great free resource: Edureka video on Data Science. You can watch an end to end, detailed and comprehensive knowledge dump on Data Science.

This Data Science video will start with basics of Statistics and Probability and then move to Machine Learning and Finally end the journey with Deep Learning and AI.