Data-Driven Decisions: A Journey Through Data Science

This comprehensive course is designed for individuals looking to enhance their data science skills. Through hands-on learning experiences and real-world case studies, you will gain an in-depth understanding of advanced analytics techniques and their applications in uncovering valuable insights from data. By the end of this course, you will have the ability to effectively use data to make informed decisions and drive data-driven solutions.

Advanced 4(3 Ratings) 185 Students enrolled English
Created by MindSparc Admin
Last updated Fri, 07-Jul-2023
+ View more
Course overview

This comprehensive data science course is aimed at individuals who are looking to take their data analysis skills to the next level. Whether you are a beginner or have some experience in data analysis, this course will provide you with a comprehensive understanding of the most advanced analytics techniques and their applications in real-world scenarios.

Throughout this course, you will gain hands-on experience in using a wide range of data science tools and techniques, including machine learning algorithms, data visualization, and statistical modeling. By working on real-world case studies, you will learn how to effectively gather, clean, and analyze data to uncover valuable insights. You will also learn how to use these insights to make informed decisions and drive data-driven solutions.

The course is designed to be both theoretical and practical, providing you with a strong foundation in data science concepts while also equipping you with the hands-on skills you need to effectively analyze and interpret data. You will have the opportunity to work on individual and team projects, allowing you to apply what you have learned to real-world data sets.

By the end of this course, you will have a deep understanding of data science and its applications, as well as the ability to effectively use data to solve complex problems. Whether you are looking to pursue a career in data science, or simply want to improve your data analysis skills for personal or professional growth, this course is the perfect way to get started.

What will i learn?

  • Apply advanced analytics techniques to real-world data sets.
  • Use machine learning algorithms to uncover insights from data.
Requirements
  • Access to a computer with internet access.
  • Familiarity with programming concepts and experience with at least one programming language.
  • Basic knowledge of statistics and probability.
Curriculum for this course
40 Lessons 24:20:59 Hours
DATA SCIENCE (descriptive, inferential, predictive, and prescriptive)
1 Lessons 00:12:25 Hours
  • Introduction to DATA SCIENCE
    00:12:25
Python for data science class
1 Lessons 00:31:24 Hours
  • Python for data science class
    00:31:24
Conditional statements in python
1 Lessons 00:35:07 Hours
  • Conditional statements
    00:35:07
Decision making statements class
1 Lessons 00:51:57 Hours
  • Decision making statements
    00:51:57
Discussion on loops class
1 Lessons 00:27:27 Hours
  • Discussion on loops
    00:27:27
Functions(definition ,uses ,creating) class
1 Lessons 00:44:02 Hours
  • functions( types of arguments )
    00:44:02
Different ways on how to reverse a given number class
1 Lessons 00:51:40 Hours
  • different ways on how to reverse a given number
    00:51:40
Infinite while , for loop using on iterative objects, multiple for loops for loop a long with range (PART-1)
1 Lessons 00:23:28 Hours
  • infinite while , for loop using on iterative objects ,multiple for loops for loop a long with range
    00:23:28
For loop that iterates over a string, for with else, looping techniques in python(enumerate, zip, sorted, reversed) (PART-2)
1 Lessons 00:23:54 Hours
  • For loop that iterates over a string ,for with else, looping techniques in python(enumerate, zip ,sorted ,reversed)
    00:23:54
Arrays(creating ,inserting ,updating ,deleting)( PART-3 )
1 Lessons 00:33:01 Hours
  • arrays(creating ,inserting, updating, deleting)
    00:33:01
Python Sets
1 Lessons 00:15:25 Hours
  • A Python set is the collection of the unordered items. Each element in the set must be unique, immutable, and the sets remove the duplicate elements. Sets are mutable which means we can modify it after its creation.
    00:15:25
Python String Methods
1 Lessons 00:35:50 Hours
  • Python String Methods and Classes and Objects
    00:35:50
Tuples in Python
1 Lessons 00:24:25 Hours
  • Tuples
    00:24:25
Dictionary methods in python
1 Lessons 00:17:00 Hours
  • Python Dictionary Methods
    00:17:00
Python Lists using for Data Science
1 Lessons 00:30:15 Hours
  • Python Lists
    00:30:15
Python Polymorphism, Inheritance and Modules
1 Lessons 00:55:04 Hours
  • Polymorphism, Inheritance and Modules
    00:55:04
NumPy Getting Started
1 Lessons 00:50:23 Hours
  • NumPy Creating Arrays, NumPy Array Indexing, NumPy Array Slicing etc.
    00:50:23
NumPy functions in Python
1 Lessons 00:38:12 Hours
  • NumPy functions in Python
    00:38:12
Pandas vs Numpy, compatibility of numpy arrays with normal python lists class
1 Lessons 00:59:36 Hours
  • pandas vs numpy, compatibility of numpy arrays with normal python lists
    00:59:36
Numpy arrays(mathematical operations ,creating n dimensional array, some inbuilt functions) class
1 Lessons 00:29:28 Hours
  • numpy arrays(mathematical operations ,creating n dimensional array, some inbuilt functions)
    00:29:28
Numpy arrays(mathematical inbuilt functions, indexing and slicing ,iterating over array, stacking together ,indexing with boolean arrays) class
1 Lessons 00:36:08 Hours
  • numpy arrays(mathematical inbuilt functions,indexing and slicing, iterating over array, stacking together ,indexing with boolean arrays)
    00:36:08
Iterate over numpy arrays ,discussion on nditer function class
1 Lessons 00:47:07 Hours
  • iterate over numpy arrays, discussion on nditer function
    00:47:07
Pandas in python for data science
1 Lessons 00:56:27 Hours
  • Pandas in python dataframes
    00:56:27
Discussion on nditer along with flags ,pandas introduction class
1 Lessons 00:29:08 Hours
  • discussion on nditer along with flags, pandas introduction
    00:29:08
Pandas( header ,skiprows, creating header ,giving names to header) class
1 Lessons 00:25:19 Hours
  • pandas( header, skiprows ,creating header ,giving names to header)
    00:25:19
Pandas(creating a data frame, inbuilt functions, accessing the columns ,conditions applied on columns) class
1 Lessons 00:37:46 Hours
  • pandas(creating a data frame ,inbuilt functions, accessing the columns, conditions applied on columns)
    00:37:46
Selecting Data From Pandas Data frames class
1 Lessons 00:27:58 Hours
  • Selecting Data From Pandas Data frames
    00:27:58
Deep Dive into pandas
3 Lessons 02:10:42 Hours
  • Creating a Series in Pandas, Creating a DataFrame in Pandas, Reading and Writing Data with Pandas, Manipulating Data with Pandas, Visualizing Data with Pandas
    00:48:27
  • Creating a Series in Pandas, Creating a DataFrame in Pandas, Reading and Writing Data with Pandas, Manipulating Data with Pandas, Visualizing Data with Pandas (PART-2)
    00:42:15
  • Creating a Series in Pandas, Creating a DataFrame in Pandas, Reading and Writing Data with Pandas, Manipulating Data with Pandas, Visualizing Data with Pandas(PART-3)
    00:40:00
Matplotlib: Visualization with Python
1 Lessons 00:50:27 Hours
  • Visualization with Python
    00:50:27
Seaborn in python
1 Lessons 00:55:07 Hours
  • seaborn vs matplotlib
    00:55:07
Introduction of probability and statistics for data science
1 Lessons 00:35:27 Hours
  • Probability and Statistics for Data Science
    00:35:27
Normal distribution, probability distribution
1 Lessons 00:35:27 Hours
  • Normal distribution, probability distribution
    00:35:27
Manipulation principles, bayes theorem in probability
1 Lessons 00:34:43 Hours
  • Manipulation principles, bayes theorem
    00:34:43
Data Science - Intro to Statistics
1 Lessons 00:35:45 Hours
  • Intro to Statistics
    00:35:45
Variance in statistics
1 Lessons 00:33:47 Hours
  • What is the variance in data science?
    00:33:47
Percentiles, quartiles and outliers
1 Lessons 00:33:50 Hours
  • Percentiles, quartiles and outliers
    00:33:50
Probability distribution function(PDF)
1 Lessons 00:30:24 Hours
  • PDF
    00:30:24
Distributions, Hypothesis testing, Pearson correlation coefficient
1 Lessons 00:35:24 Hours
  • Distributions, Hypothesis testing, Pearson correlation coefficient
    00:35:24

Frequently asked question

Who is this course designed for?
This course is designed for individuals who are looking to enhance their data science skills, including beginners with no prior experience, as well as those with some experience in data analysis who want to take their skills to the next level.
What topics will be covered in this course?
The course will cover a range of topics in data science, including advanced analytics techniques, machine learning algorithms, data visualization, and statistical modeling.
What will I be able to do by the end of this course?
By the end of this course, you will have a deep understanding of data science and its applications, as well as the ability to effectively use data to solve complex problems and make informed decisions.
+ View more
Other related courses
About instructor

MindSparc Admin

69 Reviews | 9863 Students | 16 Courses
₹14999 ₹9999
Includes: