50 Days of Data Analysis with Python: The Ultimate Challenge Book for Beginners
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50 Days of Data Analysis with Python: The Ultimate Challenge Book for Beginners

About the Book

Master Data Analysis with Python: The Ultimate 50-Day Challenge Book

Are you an aspiring data analyst, data scientist, or business analyst?

Are you a self-taught data analyst who is looking to apply your newfound skills to practical data analysis tasks?

Are you looking for a structured, practical, and hands-on approach to learning data analysis with Python?

Want to become proficient in the Python libraries used by data analysts and scientists so you can build your portfolio of projects?

If you answer yes to any of these questions, then "50 Days of Data Analysis with Python: The Ultimate Challenge Book for Beginners" is your perfect choice.

For Data Analysts and Aspiring Data Scientists

This book is perfect for beginners and aspiring data scientists alike. You'll not only conquer the basics but also dig deep into the functions most critical for data analysis. The aim of this book is to get you to a stage where you are comfortable jumping on any structured dataset and doing some analysis. This book bridges the gap between theory and practice, making it an ideal resource for those seeking to develop their data analysis skills.

Learn by doing; Solve Real-world Problems

This book is all about learning by doing. You'll tackle real-world scenarios, roll up your sleeves, and get hands-on with data analysis. Here is what you are going to do:

  • Explore real-world scenario simulations.
  • Work with diverse datasets.
  • Explore data cleaning and preprocessing.
  • Extract insights from data
  • Conduct statistical analyses.
  • Create insightful visualizations.
  • Train machine learning models
Explore Key Python Libraries

In this book, you will not only explore the main Python libraries used in data analysis, but you will also apply their numerous functions to real-world scenarios. Here is what you are going to practically learn:

  • NumPy: Effortlessly handle numerical computations.
  • pandas: Master data manipulation and analysis like a pro.
  • Seaborn: Craft captivating visualizations.
  • Matplotlib: Create stunning plots and charts.
  • Scikit-learn (Sklearn): Embrace the world of machine learning.
Sample Question

Here is one of the questions in the book:

Top Endorsement

Unleash Python's Data Analysis Capabilities

With over 300 challenges, start an engaging 50-day journey to become a skilled data analyst with Python. Remember, success comes from embracing challenges, critical thinking, exploring different approaches, and unlocking your full potential.

Start Your 50-Day Journey Today!

Don't wait—make today Day 1 of your 50 Days to supercharge your data analysis proficiency. Get your copy now and seize the opportunity for growth and success!

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  • Categories

    • Python
    • Artificial Intelligence
    • Machine Learning

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About the Author

Benjamin Bennett Alexander
Benjamin Bennett Alexander

I indulge in Tech and Finance.

Table of Contents

Contents Feedback and Reviews 3 About This Book 12 Getting Started 14 Day 1: Essentials of NumPy, Pandas, Matplotlib, Seaborn and Sklearn 16 1.0 NumPy 16 1.1 Creating NumPy Arrays 16 1.1.1 np.array() 16 1.1.2 np.arange() 17 1.1.3 np.zeros() 18 1.1.4 np.ones() 18 1.1.5 numpy.random.Generator.integers 18 1.1.6 numpy.random.Generator.random 19 1.2 Accessing Array Elements 19 1.2.1 Slicing 20 1.2.2 Fancy Indexing 20 1.2.3 Boolean Indexing 21 1.3 Array Manipulation 21 1.3.1 np.reshape() 22 1.3.2 np.concatenate() 23 1.3.3 np.split() 23 1.3.4 np.transpose() 24 1.4 Mathematical Functions 25 1.4.1 np.add() and np.subtract() 25 1.4.2 np.multiply() and np.divide() 26 1.5 Statistical Functions 27 1.5.1 np.mean() 27 1.5.2 np.median() 27 1.5.3 np.std() 28 1.5.4 np.var() 28 2.0 Pandas 29 2.1 Pandas Series 29 2.1.1 Series Index and Name 30 2.1.2 Series Data Type 30 2.2 Creating a Pandas DataFrame 32 2.3 Data Loading Functions 33 2.3.1 read_csv() 33 2.3.2 read_excel() 34 2.3.3 read_sql() 34 2.4.1 .dropna() 34 2.4.2 fillna() 36 2.4.3 bfill and ffill() 36 2.5.1 head() 37 2.5.2 tail() 38 2.5.3 info() 39 2.5.4 describe() 39 2.5.5 groupby() 40 2.5.6 merge() 41 2.6 Selecting Data 42 2.6.1 .loc 43 2.6.2 .iloc 44 2.7 Pandas Data Visualization Functions 45 2.7.1 Line Plot 45 2.7.2 Bar Plot 46 2.7.3 Box Plot 46 2.7.4 Hist Plot 48 2.8 Sorting Data 48 2.8.1 sort_values() 48 2.8.2 sort_index() 49 2.8.3 nsmallest and nlargest() 50 3.0 Matplotlib 51 3.1.1 plt.scatter() 51 3.1.2 plt.bar() 52 3.1.3 plt.hist() 53 3.1.4 plt.imshow() 54 3.1.5 plt.plot() 55 4.0 Seaborn 57 4.1.1 histplot() 57 4.1.2 lineplot() 59 4.1.3 pairplot() 59 4.1.4 regplot() 60 4.1.5 boxplot() 61 5.0 Scikit-Learn 63 5.1.1 SimpleImputer() 63 5.1.2 LabelEncoder() 64 5.1.3 OneHotEncoder() 65 5.1.4 StandardScaler() 67 5.1.5 train_test_split() 69 5.1.6 Classification with Sklearn 71 5.1.7 accuracy_score() 72 5.1.8 precision Score() 73 5.1.9 recall_score() 74 5.2.1 f1_score() 76 5.2.2 Confusion_matrix() 76 5.2.3 Regression with Sklearn 78 5.2.4 Mean Squared Error (MSE) 80 5.2.5 Root Mean Squared Error (RMSE) 80 5.2.6 R2_score 82 6.0 Final Thoughts 83 Day 2: Creating and Manipulating Arrays 84 Day 2 - Answers 85 Day 3: Generating Random Arrays 90 Day 3 - Answers 91 Day 4: NumPy Arrays and Vector Operations 94 Day 4 - Answers 95 Day 5: Array Creation and Vector Operations 100 Day 5 - Answers 101 Day 6: Array Manipulation and Vector Operations 105 Day 6 - Answers 106 Day 7: Transpose and Swap Arrays 109 Day 7 - Answers 110 Day 8: Slicing NumPy Arrays 113 Day 8 - Answers 114 Day 9: Analyze a One-Dimensional Array 117 Day 9 - Answers 118 Day 10: The arange Function and Boolean Indexing 120 Day 10 - Answers 121 Day 11: Preprocessing, Analysis and Visualization 123 Day 11 - answers 124 Day 12: Array Sorting and Filtering 127 Day 12 - Answers 128 Day 13: Slicing and Analyzing Arrays 131 Day 13 - Answers 132 Day 14: Analyze Data with NumPy Part - 1 135 Day 14 - Answers 136 Day 15: Analyse Data with NumPy Part - 2 140 Day 15 - Answers 141 Day 16: Pandas Series Analysis 145 Day 16 - Answers 146 Day 17: Creating and Modifying DataFrames 150 Day 17 - Answers 151 Day 18: Runners Data Analysis –Part 1 154 Day 18 - Answers 155 Day 19: Runners Data Analysis – Part 2 159 Day 19 - Answers 160 Day 20: Explore Data with Pandas and Matplotlib 164 Day 20 - Answers 165 Day 21: Processing Data with Pandas 169 Day 21 - Answers 170 Day 22: Data Preprocessing and Analysis 175 Day 22 - Answers 177 Day 23: Preprocessing with Pandas and Matplotlib 182 Day 23 - Answers 183 Day 24: Business Data Analysis 187 Day 24 - Answers 188 Day 25: Retail Data Processing and Analysis - Part 1 193 Day 25 - Answers 194 Day 26: Retail Data Processing and Analysis – Part 2 197 Day 26 - Answers 198 Day 27: Retail Data Processing and Analysis – Part 3 201 Day 27 - Answers 202 Day 28: Population Data Analysis 205 Day 28 - Answers 206 Day 29: Car Service Data Analysis 213 Day 29 - Answers 214 Day 30: Furniture Data Analysis 219 Day 30 - Answers 220 Day 31: Analyze Database Data with SQL 224 Day 31 - Answers 225 Day 32: Soccer Stricker’s Data Analysis 230 Day 32 - Answers 231 Day 33: Website Data Analysis 236 Day 33 - Answers 237 Day 34: Income Data Analysis 243 Day 34 - Answers 244 Day 35: Runners And Income Data Analysis 248 Day 35 - Answers 249 Day 36: Social Media Data Analysis 253 Day 36 - Answers 254 Day 37: Stock Market Data Processing and Analysis 258 Day 37 - Answers 259 Day 38: Rental Car Data Analysis 264 Day 38 - Answers 265 Day 39: Analyze, Transform, and Shift Data. 270 Day 39 - Answers 271 Day 40: Car Spare Parts Data Analysis 278 Day 40 - answers 280 Day 41: Population Data Analysis 293 Day 41 - Answers 294 Day 42: Toys Data Analysis 299 Day 42 - Answers 300 Day 43: Time Series Data Analysis 305 Day 43 - Answers 306 Day 44: Sports Data Analysis 311 Day 44 - Answers 312 Day 45: Medical Data Analysis 316 Day 45 - Answers 317 Day 46: Financial Data Analysis 320 Day 46 - Answers 321 Day 47: Text Data Preprocessing 325 Day 47 - answers 326 Day 48: Preprocess Data with Sklearn 330 Day 48 - Answers 331 Day 49: End-to-End Regression Challenge 335 Day 49 - Answers 337 Day 50: End-to-End Classification Challenge 349 Day 50 - Answers 352 What's Next? 377 Other Books By Author 380

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