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This course assumes no prior experience and can teach anyone how to use Python to analyze and visualize data. Stop using Excel and start harnessing the power of Python.
Data analysis is one of the hottest careers of the 21st century. As an analyst, your goal is to peel back layers of data in order to answer questions of interest; that is the power of analytics. It allows you to take raw data and create meaningful, actionable insights.
In this course, you’ll learn how to use Python, NumPy, SciPy, Pandas, and Seaborn to perform data analysis and visualization. You’ll explore the four crucial steps for any data analysis project: reading, describing, cleaning, and visualizing data. In each step, you will work with the most common and popular tools that data analysts use every day. By the end of the course, you will be able to confidently extract knowledge and answers from data.
def perform_calculations(array): return np.max(array), np.std(array), np.sum(array), np.dot(array, array)
def correlation(array1, array2): return stats.pearsonr(array1, array2)
def read_csv(): # Define the column names as a list names = ["mpg", "cylinders", "displacement", "horsepower", "weight", "acceleration", "model_year", "origin", "car_name"] # Read in the CSV file from the webpage using the defined column names df = pd.read_csv("auto-mpg.data", header=None, names=names, delim_whitespace=True) return df.shape
def group_aggregation(df, group_var, agg_var): grouped_df = df.groupby([group_var])[agg_var].mean() return grouped_df
def outlier_detection(df): df = df.quantile([.90, .10]) return df
Seaborn function creates scatter plots?
Q2. When is a bar plot useful?
Q3. What value is the maximum whisker value of a box plot?
Q4. When is the
map combination useful?
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This Problem is intended for audiences of all experiences who are interested in learning about Data Science in a business context; there are no prerequisites.
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