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CS250: Python for Data Science Certification Exam Answers

Python is an incredibly popular programming language for data science due to its simplicity, versatility, and a vast ecosystem of libraries tailored for data manipulation, analysis, and visualization. Here’s a breakdown of why Python is favored in the realm of data science:

  1. Ease of Learning and Use: Python’s syntax is straightforward and readable, making it accessible for beginners and enjoyable for experienced programmers. Its simplicity allows data scientists to focus more on solving problems rather than grappling with complex code.
  2. Rich Ecosystem of Libraries: Python boasts an extensive collection of libraries specifically designed for data science tasks. Some of the most prominent ones include:
    • NumPy: Provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
    • Pandas: Offers powerful data structures and data manipulation tools for structured data analysis. It’s particularly useful for tasks like data cleaning, transformation, and exploration.
    • Matplotlib and Seaborn: These libraries enable the creation of static, interactive, and publication-quality visualizations, allowing data scientists to communicate insights effectively.
    • Scikit-learn: A comprehensive machine learning library that provides various algorithms for classification, regression, clustering, dimensionality reduction, and more.
    • TensorFlow and PyTorch: Deep learning frameworks that enable building and training neural networks for tasks like image recognition, natural language processing, and reinforcement learning.
  3. Community Support and Documentation: Python has a vibrant and active community of developers and data scientists who contribute to its ecosystem. This ensures that there are ample resources, tutorials, and documentation available for both beginners and advanced users.
  4. Integration Capabilities: Python seamlessly integrates with other programming languages and tools, making it easy to incorporate data science workflows into existing systems or collaborate with colleagues using different technologies.
  5. Scalability: While Python may not be as fast as languages like C or C++, it offers sufficient performance for most data science tasks. Moreover, libraries like NumPy and Pandas are optimized for performance, and Python’s simplicity allows for easy integration with high-performance computing tools when needed.

Overall, Python’s combination of simplicity, versatility, and a rich ecosystem of libraries makes it the preferred choice for data scientists across various industries and domains.

CS250: Python for Data Science Exam Quiz Answers

  • Entering the data into a data management system
  • Putting the data into a form that allows for analysis
  • Determining the source and the form of the input data
  • It is equal to zero
  • It is less than zero
  • It is greater than zero
  • File cells
  • Session cells
  • Text cells
  • -10
  • 0
  • 10
  • random. randint (4)
  • random. randint (0,5)
  • random. randint (0,4)
  • axs [0,2}
  • axs [1,2}
  • axs [1,3}
  • shape
  • size
  • getdim
  • print (A [:3])
  • print (A [:2])
  • print (A [0:3,2])
  • A*B
  • A@B
  • A-B
  • numpy. random. random_value ()
  • numpy. random. random_number ()
  • numpy. random. random_sample ()
  • A p-value that approaches 0
  • A p-value that approaches 0.5
  • A p-value that approaches 1
  • c = scipy.stats.norm.rvs (3, numpy. sqrt (2), size=1000)
  • c = scipy.stats.norm.rvs (2, 3, size=1000)
  • c = scipy.stats.norm.rvs (3, 2, size=1000)
  • A one-dimensional array
  • A two-dimensional array
  • A multidimensional array
  • iloc
  • info
  • items
  • An exception will be generated
  • Only compatible columns will be retained
  • The new dataframe will contain missing values
  • a.to_excel (write_file_name, ‘tab1’)
  • a.to_excel (write_file_name, tab=’tab1′)
  • a.to_excel(write_file_name). make_tab(‘tab1′)
  • A line plot of all columns with horizontal axis unspecified
  • A line plot of first column with index values on the horizontal axis
  • A line plot of all columns with index values on the horizontal axis
  • histplot
  • scatterplot
  • violinplot
  • Points in swarmplot are adjusted to be non-overlapping
  • swarmplot has an input parameter for kernel estimation
  • Only swarmplot allows for horizontally rendering data points
  • An estimator with a lower bias and lower variance
  • An estimator with a higher bias and lower variance
  • An estimator with a higher bias and higher variance
  • fit
  • pca
  • sgd
  • X_normalized = preprocessing. normalize (X, norm=’l1′)
  • X_normalized = preprocessing. normalize (X, norm=’l2′)
  • X_normalized = preprocessing. normalize (X, norm=’max’)
  • Create a test set of optimally correlated values
  • Compute model performance over a range of parameter choices
  • Determine the training set pairs leading to the lowest training error
  • Supervised training algorithms are deterministic, while unsupervised training algorithms are probabilistic
  • Supervised training data requires preassigned target categories, while unsupervised training data does not require preassigned target categories
  • Supervised training methods require dimensionally reduced features, while unsupervised training methods do not require dimensionally reduced features
Python for Data Science Saylor Academy 1
  • [ 1.0 4.0 2.0 1.0 -1.5 3.0]
  • [[ 1.0 4.0]

[ 2.0 1.0]

[-1.5 3.0]]

  • [[ 1.0 2.0 -1.5]
[ 4.0 1.0 3.0]]
  • Classification labels are discrete, regression output is continuous
  • Classification models are unsupervised, regression models are supervised
  • Classification techniques require vector data, regression techniques require scalar data
Python for Data Science Saylor Academy 2
  • 0.0
  • 1.0
  • 10.0
  • add_constant
  • add_lag
  • add_mean
  • Adding values to their previous values
  • Multiplying values by their previous values
  • Subtracting values from their previous values
  • i
  • p
  • stop

going

  • random. random (0,1)
  • random. random (1)
  • random. random ()
  • import matplotlib. pyplot as plt

plt. plot ([1,2,3,4], [1,1,1,1])

  • import matplotlib. pyplot as plt

plt. plot ([1,2,3,4], [1,2,3,4])

  • import matplotlib. pyplot as plt

plt. plot ([1,1], [2,2], [3,3], [4,4])

  • print (B.max ())
  • print (B.max(axis=0))
  • print (B.max(axis=1))
  • Delete an existing file
  • Change the data type
  • Add a header to the data
  • shuffle
  • choice
  • randint
  • Its corresponding data value should be discarded.
  • Its corresponding data value has 0% confidence interval.
  • Its corresponding data value is equal to the mean.
  • iloc
  • insert
  • items
  • print (a [2:5])
  • print (a [2:5:])
  • print (a [:] [2:4])
  • Text files whose row data is separated by commas
  • SQL files whose data stored in a relational database
  • Binary data files in which row data is stored sequentially
  • df. diff. hist(bins=10)
  • df. diff (). hist (bins=10)
  • df. hist(bins=10). diff ()
  • catplot
  • distplot
  • relplot
  • Overfitting
  • Oversampling
  • Overtraining
  • dvals[np.max(test_scores)]
  • dvals [np. argmax(test_scores)]
  • dvals [np. fsolve(test_scores)]
  • By referencing the labels_ attribute
  • By creating a scatter plot of the training data
  • By computing the inverse of the clustering algorithm
  • Only K-means clustering
  • Only agglomerative clustering
  • Both K-means and agglomerative clustering
  • The sum of the residuals is minimized
  • The sum of the square of the residuals is minimized
  • The sum of the absolute value of the residuals is minimized
Python for Data Science Saylor Academy 3
  • xt = np. linspace (0.0, 10.0, 100)

yt = model. predict(xt)

  • xt = np. linspace (0.0, 10.0, 100)

xt = xt [:np. newaxis]

yt = model. predict(xt)

  • xt = np. linspace (0.0, 10.0, 100)

xt = xt [:np. newaxis]

s = model. predict (xt, yt)

  • Analyze the residuals
  • Perform cross-validation
  • Minimize mean squared error
  • sgt.pacf(tsdata, lags = 10)
  • sgt.plot.pacf(tsdata, lags = 10)
  • sgt.plot_pacf(tsdata, lags = 10)
  • To represent a system
  • To begin a data science pipeline
  • To determine patterns within data
  • On your local drive
  • On your thumb drive
  • On your Google drive
  • func
  • def
  • init
  • init
  • rand
  • seed
  • import numpy as np

A = np. array ([[0,1], [2,3], [4,5]])

  • import numpy as np

A = np. array ([[0,2,4], [1,3,5]])

  • import numpy as np

A = np. array (2,3, [0,2,4,1,3,5])

  • loadtxt and savetxt
  • loadtext and savetext
  • loadplntxt and saveplntxt
  • RandomInit
  • RandomSet
  • RandomState
  • 45
  • 95
  • 140
  • iloc
  • info
  • items
  • Add each element of c to each row of A
  • Add each element of c to each column of A
  • Concatenate the series c as a new column in A
  • import pandas as pd

df = pd. read_excel(read_file_name)

  • import pandas as pd

df = DataFRame ()

df. read_excel(read_file_name)

  • import pandas as pd

pd. read_excel (df, read_file_name)

  • histplot
  • lineplot
  • scatterplot
Python for Data Science Saylor Academy 4
  • hue
  • level
  • orient
  • feat_weight
  • min_depth
  • random_state
  • Small intra-cluster distances, large inter-cluster distances
  • Large intra-cluster distances, small inter-cluster distances
  • Large intra-cluster distances, large inter-cluster distances
  • A positive correlation coefficient implies a positive slope
  • A positive correlation coefficient implies a negative slope
  • A negative correlation coefficient implies a positive slope
  • [[1.]] [20.]
  • [[2.]] [10.]
  • [[2.]] [20.]
  • When a model perfectly learns the training set
  • When a model is inflexible to new observations
  • When the training data is too complex for the model
  • The power that the time series values are raised to
  • The pth statistical moment of the time series distribution
  • The number of previous times used to predict the present time
  • from statsmodels.tsa. model import ARIMA
  • from statsmodels.tsa. arima_model import ARIMA
  • from statsmodels.tsa. arima. model import ARIMA
Python for Data Science Saylor Academy 5
  • 0.0
  • 0.5
  • 1.0

def my_data_query (dataset_name, condition_list):

  dataset_path = ‘/var/lib/seaborn-data/’

  dataset_filename = dataset_path + dataset_name 

  df = pd. read_csv(dataset_filename)

    cylinders_condition = condition_list [0]

    weight_condition = condition_list [1]

    horsepower_condition = condition_list [2]

    filtered_df = df [(df [cylinders_condition [0]] == cylinders_condition [1]) &

                     (df [weight_condition [0]] < weight_condition [1])]

    sorted_df = filtered_df. nlargest (1, horsepower_condition [0])

    sorted_mpg_values = np. sort (sorted_df [condition_list [3]]. unique ())

    return sorted_mpg_values

def my_cluster_comparison (X_train, nc, random_state_val):

  kmns = KMeans (n_clusters=nc, random_state=random_state_val). fit(X_train)

  aggm = AgglomerativeClustering(n_clusters=nc). fit(X_train)

  n_neighbors = 1

  knn = neighbors. KNeighborsClassifier(n_neighbors)

  aggm_list = [ ] # extra storage if needed

  new_aggm_labels = np. zeros ((aggm. labels_. shape), dtype = np.int32)

  for label in range(nc):

        cluster_points = X_train [aggm. labels_ == label]

        centroid = np. mean (cluster_points, axis=0)

        knn.fit (kmns. cluster_centers_, kmns. labels_)

        nearest_neighbor_label = knn. predict([centroid])

        new_aggm_labels [aggm. labels_ == label] = nearest_neighbor_label 

  return np. where (new_aggm_labels! = kmns. labels_)

def eval_normal_pdf (x, mu, sigma):

    y1 = 1 / (sqrt (2 * pi) * sigma) * np.exp (-0.5 * ((x – mu) / sigma) **2)

    y2 = norm.pdf (x, mu, sigma)   

    return y1, y2

  • Cleansing the data
  • Creating a data plot
  • Validating a data model
  • Data sampling
  • Stratified sampling
  • Probability sampling
  • ! Pip installs
  • #Pip installs
  • @Pip installs
  • import numpy as np

A = np. linspace (0,0.1,1)

  • import numpy as np

A=np.linspace(0, 1, 10)

  • import numpy as np

A = np. linspace (0,0.1,1)

  • print (A [0:])
  • print (A [2:])
  • print (A [3:])
  • It sets the size of the marker
  • It specifies number of points to plot
  • It sets number of tickmarks for the axes
  • Outlier points
  • Kernel estimate
  • Confidence interval
  • fit
  • predict
  • make_classification
  • scaler = preprocessing. StandardScaler (). fit(X)

X_scaled = scaler. transform(X)

  • scaler = preprocessing. Normalizer (). fit(X)

X_scaled = scaler. transform(X)

  • scaler = preprocessing. QuantileTransformer (). fit(X)

X_scaled = scaler. transform(X)

  • ‘batch’
  • ‘k-means++’
  • ‘random’
  • K-means clustering requires the number of clusters as an input parameter
  • Agglomerative clustering requires the number of clusters as an input parameter
  • Both agglomerative and K-means clustering require the number of clusters as an input parameter
  • results. params [0]
  • results. params [1]
  • results. params [2]
  • Data types
  • Feature types
  • Variable types
  • 0
  • 2
  • 4
  • Save a single array to a single file in. npy format
  • Save several arrays into a single file in compressed. npy format
  • Save several arrays into a single file in uncompressed. npz format
  • A cdf estimate is plotted
  • A pdf estimate is superimposed
  • The default bin width can be modified
  • from numpy import median

import seaborn as sns

sns. barplot (x=’day’, y=’tip’, data=tips, estimator=’median’, ci = 90)

  • from numpy import median

import seaborn as sns

sns. barplot (x=’day’, y=’tip’, data=tips, estimator=median, ci = 0.90)

  • from numpy import median

import seaborn as sns

sns. barplot (x=’day’, y=’tip’, data=tips, estimator=median, ci = 90)

  • Input values are processed as scalar quantities
  • Input values are produced using nonrandom data
  • Input values are paired with desired output targets
  • n_clusters must be set to None and compute_full_tree must be set to True
  • n_clusters must be set to a value of -1 and compute_full_tree must be set to False
  • n_clusters must be set to an integer greater than one and compute_full_tree must be set to True
  • Deductive reasoning
  • Reductive reasoning
  • Subtractive reasoning
  • Because the population sample size must be verified
  • Because the deviation of the estimate must be characterized
  • Because the resulting parameters could be skewed toward the true parameters
  • kurtosis
  • skew
  • zscore
  • assign
  • fillna
  • insert
  • A cdf estimate is plotted
  • A pdf estimate is superimposed
  • The default bin width can be modified
  • Only lmplot accepts numpy arrays as input
  • Only regplot accepts numpy arrays as input
  • Both lmplot and regplot accept numpy arrays as input
  • pca = PCA (n_components = None)

pca.fit(X)

  • pca = PCA (n_components = ‘svd’)

pca.fit(X)

  • pca = PCA (n_components = ‘mle’)

pca.fit(X)

  • Recognizing images of license plates
  • Classifying objects within images of natural scenery
  • Classifying images of apples versus images of oranges
  • data. append (kmeans. mse_)
  • data. append (kmeans. delta_)
  • data. append (kmeans. inertia_)
  • A numpy array
  • A numpy scalar
  • A numpy vector
  • 1
  • 2
  • 4
  • A loss functions
  • A hypothesis tests
  • A sampling functions
  • Referring to the right plot of two plots that are placed from left to right
  • Referring to the top right corner plot of four plots placed within a square
  • Referring to the bottom plot of two plots that are stacked on top of one another
  • print (A [-1, -1])
  • print (A [-1,3])
  • print (A [3, -1])
  • hist
  • quiver
  • stem
  • A dataframe is limited to two dimensions
  • A numpy array is limited to one dimension
  • A numpy array can contain heterogeneous data
  • a.notna().count()
  • a.notna().len()
  • a.notna().sum()
  • 0
  • 1
  • 2
  • The distance between the centroids from two different clusters
  • The distance between the two closest points from two different clusters
  • The distance between the two farthest points from two different clusters
  • print (results. render ())
  • print (results. report ())
  • print (results. summary ())
  • The model coefficients are the same for each value of t
  • The value of each sample Xt is the same for each value of t
  • The mean of the distribution of each sample Xt is the same for each value of t

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