Skip to content

Data Analyst: Data Analytics+Data Visualization+Data Manipulation+Python, Numpy & Pandas

CPD Accredited Provider | Free PDF Certificate | Easy Refund Policy

Learndrive

Summary

Price
Save 65%
£12 inc VAT (was £35)
Offer ends 30 April 2024
Study method
Online, On Demand What's this?
Duration
52.1 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed courses certificate of completion - Free
Additional info
  • Tutor is available to students
20 students purchased this course

Add to basket or enquire

Overview

Data Analyst: Data Analytics+Data Visualization+Data Manipulation+Python, Numpy & Pandas

Embark on a journey into the realm of Data Analysis with our comprehensive course. Delve deeply into Data Analysis using R, gaining mastery over its intricacies and unleashing the potential of Machine Learning.

Explore the art of Python for Data Science, delve into statistical concepts, and immerse yourself in the world of predictive modelling.

Elevate your expertise in Data Science and Machine Learning with us today. Join us to embark on a journey of mastering Data Analysis like never before!

Key Performance Metrics:

  • Apply Data Analysis techniques in R for effective data manipulation.
  • Create informative Data Visualizations in R for data insights.
  • Develop Webapps with R Shiny for interactive Data Analysis.
  • Utilise Machine Learning for Data Preprocessing in Python.
  • Apply Statistics for meaningful Data Science conclusions.
  • Use NumPy for efficient Python Data Analysis.
  • Leverage Pandas for data exploration and data analysis.
  • Produce Python Data Visualizations for effective data presentation.
  • Implement advanced Machine Learning for predictive modeling.
  • Use PCA for dimensionality reduction in Data Analysis.

Accreditation:

This Data Analysis Course is CPDQE accredited, which serves as an impactful mechanism for skill enhancement.

Curriculum

36
sections
225
lectures
52h 7m
total

    • 1: Data Analysis Course Promo 03:43

    • 2: Data Analysis : Data Science ML Course Intro1 02:30
    • 3: Data Analysis : What is data science 09:47
    • 4: Data Analysis : Machine Learning Overview1 05:26
    • 5: Data Analysis : Whos this course is for1 02:57
    • 6: Data Analysis : DL and ML Marketplace1 04:38
    • 7: Data Analysis : Data Science and ML Job opps 02:36
    • 8: Data Analysis : Data Science Job Roles1 04:04

    • 9: Data Analysis : Getting Started 10:58
    • 10: Data Analysis : Basics 06:24
    • 11: Data Analysis : Files 11:08
    • 12: Data Analysis : RStudio 06:58
    • 13: Data Analysis : Tidyverse 05:19
    • 14: Data Analysis : Resources 04:02

    • 15: Data Analysis : Section Introduction 30:03
    • 16: Data Analysis : Basic Types 08:46
    • 17: Data Analysis : Vectors Part One 19:40
    • 18: Data Analysis : Vectors Part Two 24:51
    • 19: Data Analysis : Vectors - Missing Values 15:35
    • 20: Data Analysis : Vectors - Coercion 14:06
    • 21: Data Analysis : Vectors - Naming 10:15
    • 22: Data Analysis : Vectors - Misc 05:59
    • 23: Data Analysis : Creating Matrices 31:27
    • 24: Data Analysis : Lists 31:41
    • 25: Data Analysis : Introduction to Data Frames 19:20
    • 26: Data Analysis : Creating Data Frames 19:50
    • 27: Data Analysis : Data Frames_Helper Functions 31:12
    • 28: Data Analysis : Data Frames - Tibbles 39:03
    • 29: Care for a feedback? 01:00 PDF

    • 30: Data Analysis : Section Introduction Intermediate R 46:31
    • 31: Data Analysis : Relational Operations 11:06
    • 32: Data Analysis : Logical Operators 07:04
    • 33: Data Analysis : Conditional Statements 11:19
    • 34: Data Analysis : Loops 07:56
    • 35: Data Analysis : Functions 14:19
    • 36: Data Analysis : Packages 11:29
    • 37: Data Analysis : Factors 28:14
    • 38: Data Analysis : Dates and Times 30:10
    • 39: Data Analysis : Functional Programming 36:41
    • 40: Data Analysis : Data Import or Export 22:06
    • 41: Data Analysis : Database 27:08

    • 42: Data Analysis : Data Manipulation in R Section Introduction 36:29
    • 43: Data Analysis : Tidy Data 10:53
    • 44: Data Analysis : The Pipe Operator 14:50
    • 45: Data Analysis : The Filter Verb 21:34
    • 46: Data Analysis : The Select Verb 46:03
    • 47: Data Analysis : The Mutate Verb 31:56
    • 48: Data Analysis : The Arrange Verb 10:03
    • 49: Data Analysis : The Summarize Verb 23:05
    • 50: Data Analysis : Data Pivoting 42:41
    • 51: Data Analysis : JSON Parsing 10:46
    • 52: Data Analysis : String Manipulation 32:38
    • 53: Data Analysis : Web Scraping 58:53

    • 54: Data Analysis : Data Visualization in R Section Introduction 17:12
    • 55: Data Analysis : Getting Started 15:37
    • 56: Data Analysis : Aesthetics Mappings 24:44
    • 57: Data Analysis : Single Variables Plot 36:50
    • 58: Data Analysis : Two Varible Plots 20:33
    • 59: Data Analysis : Facets Layering and Coordinate System 17:56
    • 60: Data Analysis : Styling and Saving 11:33

    • 61: Data Analysis : Creating-Reports-with-R-Markdown 28:54

    • 62: Data Analysis : Section-Introduction-With-R-Shiny 26:05
    • 63: Data Analysis : A Basic App 31:18
    • 64: Data Analysis : Other Examples 34:05

    • 65: Data Analysis : Intro to Machine Learning - Part 1 21:48
    • 66: Data Analysis : Intro to Machine Learning - Part 2 46:45

    • 67: Data Analysis : Data Preprocessing 37:47
    • 68: Data Analysis : Introduction to Data Preprocessing 27:03

    • 69: Data Analysis : Linear Regression A Simple Model 53:04
    • 70: Data Analysis : LR Section Introduction 25:09

    • 71: Data Analysis : Hands-on Exploratory Data Analysis 1:02:57
    • 72: Data Analysis : Section Introduction EDA 25:03

    • 73: Data Analysis : Linear Regression - Real Model Section Intro 32:04
    • 74: Data Analysis : Linear Regression in R - real model 52:48

    • 75: Data Analysis : Introduction to Logistic Regression 37:48
    • 76: Data Analysis : Logistic Regression in R 39:37

    • 77: Data Analysis : Starting a Career in Data Science1 02:54
    • 78: Data Analysis : Data Science Resume1 03:42
    • 79: Data Analysis : Getting Started with Freelancing1 04:44
    • 80: Data Analysis : Top Freelancing Websites1 05:18
    • 81: Data Analysis : Personal Branding1 05:27
    • 82: Data Analysis : Importance of Website and Blog1 03:42
    • 83: Data Analysis : Networking dos and donts1 03:50

    • 84: Data Analysis : Who is this Course for 02:43
    • 85: Data Analysis : DS + ML Marketplace 06:55
    • 86: Data Analysis : Data Science Job Opportunities 04:24
    • 87: Data Analysis : Data Science Job Roles 10:23
    • 88: Data Analysis : What is a Data Scientist 17:00
    • 89: Data Analysis : How To Get a Data Science Job 18:39
    • 90: Data Analysis : Data Science Projects Overview 11:52

    • 91: Data Analysis : Why We Use Python 03:14
    • 92: Data Analysis : What is Data Science 13:24
    • 93: Data Analysis : What is Machine Learning 14:22
    • 94: Data Analysis : ML Concepts _ Algorithms 14:42
    • 95: Data Analysis : Machine Learning vs Deep Learning 11:09
    • 96: Data Analysis : What is Deep Learning 09:44
    • 97: Care for a feedback? 01:00 PDF

    • 98: Data Analysis : What is Python Programming 06:03
    • 99: Data Analysis : Why Python for Data Science 04:35
    • 100: Data Analysis : What is Jupyter 03:54
    • 101: Data Analysis : What is Colab 03:27
    • 102: Data Analysis : Jupyter Notebook 18:01
    • 103: Data Analysis : Getting Started with Colab 09:07
    • 104: Data Analysis : Python Variables, Booleans and None 11:47
    • 105: Data Analysis : Python Operators 25:26
    • 106: Data Analysis : Python Numbers and Booleans 07:47
    • 107: Data Analysis : Python Strings 13:12
    • 108: Data Analysis : Python Conditional Statements 13:53
    • 109: Data Analysis : Python For Loops and While Loops 08:07
    • 110: Data Analysis : Python Lists 05:10
    • 111: Data Analysis : More About Python Lists 15:08
    • 112: Data Analysis : Python Tuples 11:25
    • 113: Data Analysis : Python Dictionaries 20:19
    • 114: Data Analysis : Python Sets 09:41
    • 115: Data Analysis : Compound Data Types and When to use each Data Type 12:58
    • 116: Data Analysis : Functions 14:23
    • 117: Data Analysis : Python Object Oriented Programming 18:47

    • 118: Data Analysis : Intro to Statistics 07:10
    • 119: Data Analysis : Descriptive Statistics 06:35
    • 120: Data Analysis : Measure of Variability 12:19
    • 121: Data Analysis : Measure of Variability Continued 09:35
    • 122: Data Analysis : Measures of Variable Relationship 07:37
    • 123: Data Analysis : Inferential Statistics 15:18
    • 124: Data Analysis : Measures of Asymmetry 01:57
    • 125: Data Analysis : Sampling Distribution 07:34

    • 126: Data Analysis : What Exactly Probability 03:44
    • 127: Data Analysis : Expected Values 02:38
    • 128: Data Analysis : Relative Frequency 05:15
    • 129: Data Analysis : Hypothesis Testing Overview 09:09

    • 130: Data Analysis : NumPy Array Data Types 12:58
    • 131: Data Analysis : NumPy Arrays 08:21
    • 132: Data Analysis : NumPy Array Basics 11:36
    • 133: Data Analysis : NumPy Array Indexing 09:10
    • 134: Data Analysis : NumPy Array Computations 05:53
    • 135: Data Analysis : Broadcasting 04:32

    • 136: Data Analysis : Intro to Pandas 15:52
    • 137: Data Analysis : Intro to Panda Continued 18:05

    • 138: Data Analysis : Data Visualization Overview 24:49
    • 139: Data Analysis : Different Data Visualization Libraries in Python 12:48
    • 140: Data Analysis : Python Data Visualization Implementation 08:27

    • 141: Data Analysis : Intro to ML 26:03

    • 142: Data Analysis : Exploratory Data Analysis 13:05

    • 143: Data Analysis : Feature Scaling 07:40
    • 144: Data Analysis : Data Cleaning 07:43

    • 145: Data Analysis : Feature Engineering 06:11

    • 146: Data Analysis : Linear Regression Intro 08:17
    • 147: Data Analysis : Gradient Descent 05:58
    • 148: Data Analysis : Linear Regression + Correlation Methods 26:33
    • 149: Data Analysis : Linear Regression Implementation 05:06
    • 150: Data Analysis : Logistic Regression 03:22

    • 151: Data Analysis : KNN Overview 03:01
    • 152: Data Analysis : Parametic vs Non-Parametic Models 03:28
    • 153: Data Analysis : EDA on Iris Dataset 22:08
    • 154: Data Analysis : KNN - Intuition 02:16
    • 155: Data Analysis : Implement the KNN algorithm from scratch 11:45
    • 156: Data Analysis : Compare the Reuslt with Sklearn Library 03:47
    • 157: Data Analysis : KNN Hyperparameter tuning using the cross-validation 10:47
    • 158: Data Analysis : The decision boundary visualization 04:55
    • 159: Data Analysis : KNN - Manhattan vs Euclidean Distance 11:20
    • 160: Data Analysis : KNN Scaling in KNN 06:01
    • 161: Data Analysis : Curse of dimensionality 08:09
    • 162: Data Analysis : KNN use cases 03:32
    • 163: Data Analysis : KNN pros and cons 05:32

    • 164: Data Analysis : Decision Trees Section Overview 04:11
    • 165: Data Analysis : EDA on Adult Dataset 16:53
    • 166: Data Analysis : What is Entropy and Information Gain 21:50
    • 167: Data Analysis : The Decision Tree ID3 algorithm from scratch Part 1 11:32
    • 168: Data Analysis : The Decision Tree ID3 algorithm from scratch Part 2 07:35
    • 169: Data Analysis : The Decision Tree ID3 algorithm from scratch Part 3 04:07
    • 170: Data Analysis : ID3 - Putting Everything Together 21:23
    • 171: Data Analysis : Evaluating our ID3 implementation 16:51
    • 172: Data Analysis : Compare with Sklearn implementation 08:51
    • 173: Data Analysis : Visualizing the Tree 10:15
    • 174: Data Analysis : Plot the features importance 05:51
    • 175: Data Analysis : Decision Trees Hyper-parameters 11:39
    • 176: Data Analysis : Pruning 17:11
    • 177: Data Analysis : [Optional] Gain Ration 02:49
    • 178: Data Analysis : Decision Trees Pros and Cons 07:31
    • 179: Data Analysis : [Project] Predict whether income exceeds $50Kyr - Overview 02:33

    • 180: Data Analysis : Ensemble Learning Section Overview 03:46
    • 181: Data Analysis : What is Ensemble Learning 13:06
    • 182: Data Analysis : What is Bootstrap Sampling 08:25
    • 183: Data Analysis : What is Bagging 05:20
    • 184: Data Analysis : Out-of-Bag Error 07:47
    • 185: Data Analysis : Implementing Random Forests from scratch Part 1 22:34
    • 186: Data Analysis : Implementing Random Forests from scratch Part 2 06:10
    • 187: Data Analysis : Compare with sklearn implementation 03:41
    • 188: Data Analysis : Random Forests Hyper-Parameters 04:23
    • 189: Data Analysis : Random Forests Pros and Cons 05:25
    • 190: Data Analysis : What is Boosting 04:41
    • 191: Data Analysis : AdaBoost Part 1 04:10
    • 192: Data Analysis : AdaBoost Part 2 14:33

    • 193: Data Analysis : SVM - Outline 05:15
    • 194: Data Analysis : SVM - SVM intuition 11:38
    • 195: Data Analysis : SVM - Hard vs Soft Margin 13:25
    • 196: Data Analysis : SVM - C HP 04:17
    • 197: Data Analysis : SVM - Kernel Trick 12:18
    • 198: Data Analysis : SVM - Kernel Types 18:13
    • 199: Data Analysis : SVM - Linear Dataset 13:35
    • 200: Data Analysis : SVM - Non-Linear Dataset 12:50
    • 201: Data Analysis : SVM with Regression 05:51
    • 202: Data Analysis : SVM - Project Overview 04:26

    • 203: Data Analysis : Unsupervised Machine Learning Intro 20:22
    • 204: Data Analysis : Representation of Clusters 20:48
    • 205: Data Analysis : Data Standardization 19:05

    • 206: Data Analysis : PCA - Section Overview 05:12
    • 207: Data Analysis : What is PCA 09:36
    • 208: Data Analysis : PCA - Drawbacks 03:31
    • 209: Data Analysis : PCA - Algorithm Steps 13:12
    • 210: Data Analysis : PCA - Cov vs SVD 04:58
    • 211: Data Analysis : PCA - Main Applications 02:50
    • 212: Data Analysis : PCA - Image Compression Scratch 27:00
    • 213: Data Analysis : PCA - Data Preprocessing Scratch 14:31
    • 214: Data Analysis : PCA - BiPlot 17:27
    • 215: Data Analysis : PCA - Feature Scaling and Screeplot 09:29
    • 216: Data Analysis : PCA - Supervised vs unsupervised 04:55
    • 217: Data Analysis : PCA - Visualization 07:31

    • 218: Data Analysis : Creating a Data Science Resume 06:45
    • 219: Data Analysis : Data Science Cover Letter 03:33
    • 220: Data Analysis : How To Contact Recruiters 04:20
    • 221: Data Analysis : Getting Started with Freelancing 04:13
    • 222: Data Analysis : Top Freelance Websites 05:35
    • 223: Data Analysis : Personal Branding 04:02
    • 224: Data Analysis : Networking Do_s and Don_ts 03:45
    • 225: Data Analysis : Importance of a Website 02:56

Course media

Description

Key Lesson Snippets:

Fundamentals of Data Analysis: Understand the core concepts of Data Analysis, including data science and machine learning.

R Programming Basics: Learn the essentials of R programming, a crucial tool in data science and Data Analysis.

Data Preprocessing: Master the practical aspect of data processing, a vital component in machine learning and Data Analysis.

Linear Regression: Explore practical applications of machine learning with linear regression models in Data Analysis.

Logistic Regression: Delve into logistic regression for solving classification problems in data science and Data Analysis.

Python for Data Science: Discover the role of Python in data science and its career prospects in Data Analysis.

Python Proficiency: Gain proficiency in using Python for various data science tasks, from basic to advanced, in Data Analysis.

Statistics Analysis: Apply statistical skills effectively in data science and Data Analysis, covering descriptive and inferential statistics.

Probability and Hypothesis Testing: Learn about probability, hypothesis testing, and their applications in data analysis and Data Analysis.

Machine Learning: Dive deeper into machine learning, covering data loading, exploration, cleaning, and various algorithms in Data Analysis.

Who is this course for?

This course will pave the way for higher study in the field of Data Analysis. You can enrol on higher level Data Analysis courses, such as:

  • Level 3 Diploma in Data Analysis
  • Level 3 Certificate in Data Analysis
  • Level 4 Diploma in Data Analysis
  • Level 4 Certificate in Data Analysis
  • Level 5 Diploma in Data Analysis
  • Level 5 Certificate in Data Analysis
  • Level 6 Diploma in Data Analysis
  • Level 6 Certificate in Data Analysis
  • Level 7 Diploma in Data Analysis and Machine Learning
  • Level 7 Certificate in Data Analysis and Machine Learning
  • Level 7 Diploma in Data Analysis for Business
  • Level 7 Certificate in Data Analysis for Business
  • Level 8 Diploma in Data Analysis and Machine Learning
  • Level 8 Certificate in Data Analysis and Machine Learning
  • Level 8 Diploma in Data Analysis for Business
  • Level 8 Certificate in Data Analysis for Business

Career path

  • Data analyst - £34,100 per annum
  • Data Scientist - £50,618 per annum
  • Data Engineer - £60,084per annum
  • Data Analytics Consultant - £56,444 per annum
  • Machine Learning Engineer - £56,631 per annum
  • Data science intern - £20,203 per annum

Questions and answers

Currently there are no Q&As for this course. Be the first to ask a question.

Ask a question

Certificates

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

Reviews

4.6
Course rating
80%
Service
100%
Content
100%
Value

Legal information

This course is advertised on reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

Study method describes the format in which the course will be delivered. At Reed Courses, courses are delivered in a number of ways, including online courses, where the course content can be accessed online remotely, and classroom courses, where courses are delivered in person at a classroom venue.

CPD stands for Continuing Professional Development. If you work in certain professions or for certain companies, your employer may require you to complete a number of CPD hours or points, per year. You can find a range of CPD courses on Reed Courses, many of which can be completed online.

A regulated qualification is delivered by a learning institution which is regulated by a government body. In England, the government body which regulates courses is Ofqual. Ofqual regulated qualifications sit on the Regulated Qualifications Framework (RQF), which can help students understand how different qualifications in different fields compare to each other. The framework also helps students to understand what qualifications they need to progress towards a higher learning goal, such as a university degree or equivalent higher education award.

An endorsed course is a skills based course which has been checked over and approved by an independent awarding body. Endorsed courses are not regulated so do not result in a qualification - however, the student can usually purchase a certificate showing the awarding body's logo if they wish. Certain awarding bodies - such as Quality Licence Scheme and TQUK - have developed endorsement schemes as a way to help students select the best skills based courses for them.
View all
Related searches: Business analysis courses, Analytics courses, Online courses