{"id":228,"date":"2021-09-13T08:48:13","date_gmt":"2021-09-13T08:48:13","guid":{"rendered":"https:\/\/www.kindsonthegenius.com\/data-science\/?p=228"},"modified":"2021-09-25T13:47:32","modified_gmt":"2021-09-25T13:47:32","slug":"practical-data-science-class-for-data-science-beginners","status":"publish","type":"post","link":"https:\/\/www.kindsonthegenius.com\/data-science\/practical-data-science-class-for-data-science-beginners\/","title":{"rendered":"Class 1 – Practical Data Science Class For Data Science Beginners"},"content":{"rendered":"

This would be your first class as a beginner Data Scientist. It would be a practical class with some explanations of the concepts along the line.<\/p>\n

Here’s what we’ll cover in this class:<\/p>\n

    \n
  1. Obtain Free Dataset from sklearn<\/a><\/li>\n
  2. Slicing Your Data<\/a><\/li>\n
  3. Create a Dictionary Using the Dataset<\/a><\/li>\n
  4. Convert to Pandas Dataframe<\/a><\/li>\n
  5. Replace Numerical Values with Target Names<\/a><\/li>\n
  6. Write data to csv<\/a><\/li>\n
  7. Check dimension of the dataset<\/a><\/li>\n
  8. View data types<\/a><\/li>\n
  9. View summary of dataset<\/a><\/li>\n
  10. Check class distribution of data using group_by<\/a><\/li>\n
  11. Check Correlation between features<\/a><\/li>\n
  12. Check skewness of dataset<\/a><\/li>\n<\/ol>\n

    Note<\/strong>: This class is clearer when you also watch the video lesson.<\/p>\n

    Class 1 Video<\/a><\/p>\n

    1. Obtain Free Dataset from sklearn<\/strong><\/h4>\n

    There are a number of ways to get free datasets. You can also generate your own dataset. One way to get free datasets is to get them from packages in R. This is explained here<\/a>.<\/p>\n

    But in this tutorial, we would the iris dataset from sklearn. The dataset comes as an array. The code is given below:<\/p>\n

    import<\/span> sklearn.datasets<\/span> as<\/span> ds<\/span>\r\niris =<\/span> ds.<\/span>load_iris()\r\n<\/pre>\n
    The above code gets the dataset and loads it into the variable iris.<\/p>\n
     <\/p>\n
    2. Slicing Your Data<\/strong><\/h4>\n
    Slicing simply means taking a subset of the dataset. The code below separates the dataset into columns:<\/p>\n
    col0 =<\/span> iris.<\/span>data[:,0<\/span>] # column 0<\/span>\r\ncol1 =<\/span> iris.<\/span>data[:,1<\/span>] # column 1<\/span>\r\ncol2 =<\/span> iris.<\/span>data[:,2<\/span>] # column 2<\/span>\r\ncol3 =<\/span> iris.<\/span>data[:,3<\/span>] # column 3<\/span>\r\n<\/pre>\n
     <\/p>\n
    3. Create a Dictionary Using the Dataset<\/strong><\/h4>\n
    Now we create a python dictionary called iris_dict. We need a dictionary for us to covert the array dataset to a pandas DataFrame<\/p>\n
    iris_dict =<\/span> {'Sepal Length'<\/span>:col[:,0<\/span>], \r\n             'Sepal Width'<\/span>:col[:,1<\/span>], \r\n             'Petal Length'<\/span>:col[:,2<\/span>], \r\n             'Petal Width'<\/span>:col[:,3<\/span>], \r\n             'Target'<\/span>:iris.<\/span>target\r\n            }\r\n<\/pre>\n
     <\/p>\n
    4. Convert to Pandas Dataframe<\/strong><\/h4>\n
    We do this using the code below.<\/p>\n
    import<\/span> pandas<\/span> as<\/span> pd<\/span>\r\niris_df =<\/span> pd.<\/span>DataFrame(data=<\/span>iris_dict)\r\n<\/pre>\n
    The new dataframe is called iris_df.<\/p>\n
     <\/p>\n
    5. Replace Numerical Values With Text Target Names<\/strong><\/h4>\n
    We would now have to replace the numerical values (0, 1, 2) with the actual names of the classes available in iris.target_names. The code below does that<\/p>\n
    <\/p>\n
    # Replace Numerical classes with Target names<\/span>\r\ntarget =<\/span> iris.<\/span>target_names\r\n\r\niris_df.<\/span>loc[iris_df['Target'<\/span>]==<\/span>0<\/span>, 'Target'<\/span>] =<\/span> target[0<\/span>]\r\niris_df.<\/span>loc[iris_df['Target'<\/span>]==<\/span>1<\/span>, 'Target'<\/span>] =<\/span> target[1<\/span>]\r\niris_df.<\/span>loc[iris_df['Target'<\/span>]==<\/span>2<\/span>, 'Target'<\/span>] =<\/span> target[2<\/span>]\r\n<\/pre>\n
     <\/p>\n
    6. Write Pandas DataFrame to csv<\/strong><\/h4>\n
    Now you can export this data as a csv in your local computer<\/p>\n
    iris_df.<\/span>to_csv('irisCSV.csv'<\/span>)\r\n<\/pre>\n
    It is saved in the same directory as the current notebook you are working with.<\/p>\n
     <\/p>\n
    6. Check dimension of the dataset<\/strong><\/h4>\n
    Dimension of the dataset is simply the number of rows and columns in the dataset. You get it using the shape method as shown below:<\/p>\n
    iris_df.<\/span>shape\r\n<\/pre>\n
     <\/p>\n
     <\/p>\n
    8. View data types<\/strong><\/h4>\n
    This means that you want to know the datatypes of the columns in your dataset. You get it using the dtypes method<\/p>\n
    iris_df.<\/span>dtypes\r\n<\/pre>\n
    The output will be:<\/p>\n
    Sepal Length    float64\r\nSepal Width     float64\r\nPetal Length    float64\r\nPetal Width     float64\r\nTarget            int64\r\ndtype: object\r\n<\/pre>\n
     <\/p>\n
    9. View summary of dataset<\/strong><\/h4>\n
    We can get the summary statistics of our dataset. These statistics includes mean, count, etc<\/p>\n
    from<\/span> pandas<\/span> import<\/span> set_option # allows us to set precision<\/span>\r\nset_option('precision'<\/span>, 2<\/span>)\r\niris_df.<\/span>describe()\r\n<\/pre>\n
    The output of the above code is :<\/p>\n
    \"Output<\/a>
    Output of dataset summary using describe()<\/figcaption><\/figure>\n
     <\/p>\n
    10. Check class distribution of data using group_by<\/strong><\/h4>\n
    The class distribution helps you to see the balance of the class values. See the video for more explanation.<\/p>\n
    iris_df.<\/span>groupby('Target'<\/span>).<\/span>size()\r\n<\/pre>\n
    The output would be
    \n<\/p>\n
    Target\r\n0    50\r\n1    50\r\n2    50\r\ndtype: int64\r\n<\/pre>\n
     <\/p>\n
    11. Check Correlation between features<\/strong><\/h4>\n
    Correlation is the relationship between the variables in your dataset. The values of correlation ranges from -1 (negative correlation) to 0 (no correlation) to 1 (positive correlation).<\/p>\n
    correlations =<\/span> iris_df.<\/span>corr(method=<\/span>'pearson'<\/span>)\r\ncorrelations # you can also use print(correlations)<\/span>\r\n<\/pre>\n
    The output of the above code is given below:<\/p>\n
    \"Output<\/a>
    Output of Correlation<\/figcaption><\/figure>\n
     <\/p>\n
    12. Check skewness of dataset<\/strong><\/h4>\n
    Skewness of the data is the distribution of the data that is expected to be a normal distribution (Gaussian) but it appear distorted or shifted to either the left or the right.<\/p>\n
    iris_df.<\/span>skew()\r\n<\/pre>\n
    The output of the above code is given below:<\/p>\n
    Sepal Length    0.31\r\nSepal Width     0.32\r\nPetal Length   -0.27\r\nPetal Width    -0.10\r\nTarget          0.00\r\ndtype: float64\r\n<\/pre>\n
     <\/p>\n","protected":false},"excerpt":{"rendered":"
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