I Feel:

我觉得：

In today’s digital world data has become as important as air. Machines & humans both are literally breathing in & breathing out data, data & data….

在当今的数字世界中，数据已变得与空气一样重要。 机器和人类实际上都是在吸入和吸入数据，数据和数据……。

People are consuming and generating huge volumes of data knowingly and unknowingly on a daily basis. It is this bombardment of digital information is that current businesses are trying to tap and harness to sell and engage their customers more. All types of Industries are bringing a personal touch into their services and offerings to give awesome user experience to their customers. All these have become possible due to powerful Data science enabled AI/ML techniques that are empowering our machines, allowing them to make analytical decisions based on a sea of data accessible to them.

人们每天都在有意无意地消耗和生成大量数据。 正是对数字信息的轰炸表明，当前的企业正试图利用和利用其来销售和吸引更多的客户。 所有类型的行业都在其服务和产品中加入个人风格，以为其客户提供绝佳的用户体验。 由于强大的启用了数据科学的AI / ML技术为我们的机器提供了支持，使它们能够基于可访问的大量数据做出分析决策，所有这些都成为可能。

In order to analyze these huge data sets our machines make use of some really powerful data visualization packages built-in Python. So we will try to capture

为了分析这些巨大的数据集，我们的机器使用了一些内置Python的功能非常强大的数据可视化程序包。 因此，我们将尝试捕获

1. What Is Data Visualization?

1.什么是数据可视化？

2. What are Data Visualization Packages?

2.什么是数据可视化包？

3. How To Use Them?

3.如何使用它们？

4. Why Should You Learn Them?

4.为什么要学习它们？

In this series on Data visualization using python which we will break in many parts.

在关于使用python进行数据可视化的系列文章中，我们将分为许多部分。

数据科学中的数据可视化： (Data Visualization In Data Science:)

As we know our human mind is trained to understand more by images. So the saying goes “A picture is worth a thousand words”. This is completely relevant when you are learning Data Science. You will be dealing with a large volume of data sets that need visual expression to make some sense in deducing valuable hidden patterns.

众所周知，我们的大脑经过训练可以通过图像进一步理解。 因此俗话说“ 一张图片值得一千个字 ”。 当您学习数据科学时，这是完全相关的。 您将要处理大量的数据集，这些数据集需要直观的表达才能在推断出有价值的隐藏模式时有意义。

Data visualization is a technique in the data science field, allowing you to tell a compelling story, visualizing data, and findings in an approachable and stimulating way. It makes complex data look simple and easy to understand.

数据可视化是数据科学领域中的一种技术，它使您能够以一种易于接近且令人振奋的方式讲述一个引人入胜的故事，可视化数据和发现。 它使复杂的数据看起来简单易懂。

数据可视化工具： (Data Visualization Tools:)

We will try to cover some of the popular data visualization tools givens below

我们将尝试介绍以下一些流行的数据可视化工具

1. Matplotlib

   Matplotlib

2. Seaborn

   Seaborn

3. Plotly

   密谋

4. Pandas

   大熊猫

Learning how to leverage these software tools to visualize data will help you make sense of data, extract meaningful information, and plot it visually to make more effective data-driven decisions.

学习如何利用这些软件工具来可视化数据将帮助您理解数据，提取有意义的信息并对其进行直观地绘制以做出更有效的数据驱动决策。

So let’s get started with Matplotlib which we will cover in today’s piece of article, rest we will cover in an upcoming series of Data visualization.

因此，让我们开始使用Matplotlib，我们将在今天的文章中介绍它，其余的将在以后的数据可视化系列文章中介绍。

答：Matplotlib： (A: Matplotlib:)

As per official MatplotlibPortal:

根据官方Matplotlib门户网站：

Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

Matplotlib是一个Python 2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境生成出版物质量的图形。 Matplotlib可用于Python脚本，Python和IPython外壳，Jupyter笔记本，Web应用程序服务器以及四个图形用户界面工具包。

Matplotlib is a widely used tool for data visualization, which works great at low-level with a Matlab like GUI interface and offers you a lot of flexibility in terms of writing code, yes, it can be sometimes tedious writing more codes but it is worth with the kind of freedom it gives.

Matplotlib是数据可视化的一种广泛使用的工具，它在Matlab之类的GUI界面下可以很好地在低级运行，并为您提供了很多编写代码方面的灵活性，是的，有时候编写更多代码可能很乏味，但是值得赋予它的自由。

安装Matplotlib： (Installing Matplotlib:)

1. Using PIP:

   使用画中画：

python -m pip install -U pip

python -m pip install -U pip

python -m pip install -U matplotlib

python -m pip install -U matplotlib

2.使用科学的Python发行版： (2. Using Scientific Python Distribution :)

There are many third-party scientific distributions like

有很多第三方科学发行版，例如

• Anaconda

  水蟒

• Canopy

  天篷

• Activestate

  活跃状态

Anaconda is my personal favorite, it is one of the popular python data science distributions which gives you a hassle-free installation of all data science related packages and comes pre-loaded with Numpy, SciPy, Pandas, Matplotlib, Plotly, etc. I would recommend you all to install it and you will be all set in a few seconds.

Anaconda是我个人的最爱，它是最受欢迎的python数据科学发行版之一，可让您轻松安装所有与数据科学相关的软件包，并预装了Numpy，SciPy，Pandas，Matplotlib，Plotly等 。 我建议大家安装它，几秒钟之内就可以设置好。

You can install any package using Conda command prompt/terminal by using conda terminal, though you need to visit the package official site, to get the exact command format.

您可以使用conda终端使用C onda命令提示符/终端来安装任何软件包，尽管您需要访问软件包的官方网站以获取确切的命令格式。

• conda install PackageName

  conda安装PackageName

For Matplotlib:

对于Matplotlib：

• conda install matplotlib

  conda安装matplotlib

Various types of data visualization Matplotlib provides are :

Matplotlib提供的各种类型的数据可视化包括：

1. Lines, bars and markers线，条和标记
2. Images, contours & fields图像，轮廓和场
3. Pie & polar charts饼图和极坐标图
4. Statistical level Plotting统计水平图

& many more.

＆ 还有很多。

They are widely used for line charts, bar charts, histograms, pie-chart, etc..

它们被广泛用于折线图，条形图，直方图，饼图等。

For detail visit gallery section by clicking on the link below gallery — Matplotlib 3.1.0 documentation

有关详细信息，请单击图库下面的链接来访问图库部分— Matplotlib 3.1.0文档

This gallery contains examples of the many things you can do with Matplotlib. Click on any image to see the full image…matplotlib.org

该库包含您可以使用Matplotlib进行的许多操作的示例。 单击任何图像以查看完整图像……matplotlib.org

用MatplotLib绘图：让我们通过示例学习： (Plotting With MatplotLib: Let’s Learn By Examples:)

As discussed, Matplotlib facilitates various kinds of plot ranging from scatter plots, to bar charts, to the histogram. The selection is totally contextual and is made based on our data visualization requirements like group comparison, comparing two quantitative variables to each other, or to understand data distribution, etc.

如所讨论的，Matplotlib促进了各种散点图，从散点图到条形图再到直方图。 选择完全是上下文相关的，是根据我们的数据可视化要求(例如组比较，将两个定量变量相互比较或了解数据分布等)进行的。

We will cover a few popular plotting techniques here:

我们将在这里介绍一些流行的绘图技术：

基本要求 ： (Basic Requirements :)

Before we start getting our hands dirty with some real examples, we need to be ready with few installations :

在开始使用一些真实的例子开始学习之前，我们需要准备很少的安装：

安装Anaconda发行版： (Install Anaconda Distribution:)

1. First, you need to ensure anaconda is installed :

1.首先，您需要确保安装了anaconda：

Use the given link below to learn the installation process. It is easy and you can get started in a few seconds: Installation — Anaconda 2.0 documentation

使用下面的给定链接了解安装过程。 这很容易，您可以在几秒钟内上手： 安装— Anaconda 2.0文档

On Windows, macOS, and Linux, it is best to install Anaconda for the local user, which does not require administrator…docs.anaconda.com

在Windows，macOS和Linux上，最好为本地用户安装Anaconda，这不需要管理员…docs.anaconda.com

启动Jupyter Notebook： (Launch Jupyter Notebook:)

Once you are done with the installation of anaconda distribution, open the anaconda navigator on your computer and launch Jupyter notebook as shown in the image below. We will be using the Jupyter notebook to code our examples.

完成anaconda发行版的安装后，请在计算机上打开anaconda导航器，然后启动Jupyter笔记本电脑，如下图所示。 我们将使用Jupyter笔记本编写示例代码。

检查必备软件包的安装： (Check for the Prerequisite Package Installation:)

Refer to the below-given image: Go to Environments menu option and you will see various pre-installed packages on the right. For eg. Search for Pandas and you will see that it is pre-installed, similarly, you can type in the required package and discover them to install if not already installed though Anaconda Navigator. Check and ensure Matplotlib, NumPy, pandas, seaborn, etc are pre-installed and install them if it is not installed.

请参考下图：转到Environments菜单选项，您将在右侧看到各种预安装的软件包。 例如。 搜索熊猫，您将看到它已预安装，类似地，您可以键入所需的软件包，如果尚未通过Anaconda Navigator安装，则可以发现要安装的软件包。 检查并确保预先安装了Matplotlib，NumPy，pandas，seaborn等，如果未安装，请安装它们。

Once you are done with required package installation, let’s get started with our first plot called Bar Chart:

完成必需的软件包安装后，让我们开始第一个图表，称为“ 条形图”：

有关Matplotlibs的一些要点要记住： (Some Key Points About Matplotlibs To Be Remembered:)

Matplotlib has an important module named PyPlot, which aids in plotting figures. The Jupyter notebook can be used for running the plots, it gives hassle-free experience and is easy to get started. We have to import matplotlib.pyplot as plt for making it call the package module.

Matplotlib有一个名为PyPlot的重要模块，可帮助绘制图形。 Jupyter笔记本可用于运行绘图，它提供了无忧的体验，并且易于入门。 我们必须将matplotlib.pyplot导入为plt ，以使其称为包模块。

• You can Import required libraries and dataset to plot using Pandas pd.read_csv()

  您可以导入所需的库和数据集以使用Pandas pd.read_csv()进行绘图

• Use plt.plot()for plotting line chart similarly in place of plot other functions are used for plotting. All plotting functions require data and it is provided in the function through parameters.

  类似地， 使用plt.plot()绘制折线图来代替绘图，其他函数也可以用于绘图。 所有绘图功能都需要数据，并且通过参数在功能中提供数据。

• Use plot.xlabel , plt.ylabel for labeling x and y-axis respectively.

  使用plot.xlabel和plt.ylabel分别标记x和y轴。

• Use plt.xticks , plt.yticks for labeling x and y-axis observation tick points respectively.

  使用plt.xticks和plt.yticks分别标记x和y轴观察刻度点。

• Use plt.legend() for signifying the observation variables.

  使用plt.legend()表示观察变量。

• Use plt.title() for setting the title of the plot.

  使用plt.title()设置图的标题。

• Use plot.show() for displaying the plot.

  使用plot.show()显示图。

1.条形图绘制： (1. Bar Chart Plotting:)

条形图示例： (Bar Plotting Example :)

#Here we import the matplotlib package with alias name as plt

＃这里我们导入别名为plt的matplotlib包

import matplotlib.pyplot as pltplt.bar([1,3,5,7,9],[5,2,7,8,2], label=”Example one”)plt.bar([2,4,6,8,10],[8,6,2,5,6], label=”Example two”, color=’g’)plt.legend()plt.xlabel(‘bar number’)plt.ylabel(‘bar height’)plt.title(‘Wow! We Got Our First Bar Graph’)plt.show()

Copy the above code and paste it in your Jupyter notebook, run it and you will be able to see the bar plot visuals as shown below:

复制上面的代码并将其粘贴到Jupyter笔记本中，运行它，您将能够看到条形图的视觉效果，如下所示：

说明： (Explanation:)

After we import matplotlib data visualization package its submodule pyplot has got this bar method which helps you plot a basic bar graph ;

导入matplotlib数据可视化软件包后，其子模块pyplot获得了此bar方法，可帮助您绘制基本的条形图；

Here plt. bar method can be better understood by the explanation given below.

这里PLT。 通过下面给出的说明可以更好地理解bar方法。

#matplotlib.pyplot.bar(x, height, width=0.8, bottom=None, *, align=’center’, data=None, **kwargs)[source]

So to make a bar plot:

因此，要制作条形图：

The bars are positioned at x with the given alignment. Their dimensions are given by width and height. The vertical baseline is bottom(default 0).

这些条以给定的对齐方式位于x处 。 它们的尺寸由宽度和高度给定。 垂直基线为底部 (默认为0)。

Each of x, height, width, and bottom may either be a scalar applying to all bars, or it may be a sequence of length N providing a separate value for each bar.

x ， height ， width和bottom的每个可以是应用于所有条形的标量，也可以是长度N的序列，为每个条形提供单独的值。

For more detail visit:matplotlib.pyplot.bar — Matplotlib 3.1.0 documentation

有关更多详细信息，请访问： matplotlib.pyplot.bar — Matplotlib 3.1.0文档

The optional arguments color, edgecolor, linewidth, xerr, and yerr can be either scalars or sequences of length equal…matplotlib.org

可选参数color，edgecolor，linewidth，xerr和yerr可以是标量，也可以是长度相等的序列... matplotlib.org

2.直方图： (2. Histogram:)

A histogram is a plot of the frequency distribution of a numeric array by splitting it into small equal-sized bins.Histograms are used to estimate the distribution of the data, with the frequency of values assigned to a value range called a bin.

直方图是将数字数组的频率分布划分为相等大小的小块而得到的图。直方图用于估计数据的分布，将值的频率分配给一个称为bin的值范围。

If you want to mathematically split a given array to bins and frequencies, use the numpy’s histogram() method . If you want to measure distribution of numeric values you can do so with .hist() plot method to create a simple histogram

如果要数学上将给定的数组拆分为频段和频率，请使用numpy的histogram()方法。 如果要测量数值的分布，可以使用.hist()绘图方法来创建简单的直方图

Matplotlib provides the functionality to visualize Python histograms out of the box with a versatile wrapper around NumPy’s histogram():

Matplotlib通过围绕NumPy的histogram()的通用包装器，提供了开箱即用地可视化Python直方图的功能：

例： (Example:)

#Histogram Codeimport matplotlib.pyplot as pltimport numpy as np #importing numpy package for array generationnp.set_printoptions(precision=3)>>> d = np.random.laplace(loc=15, scale=3, size=500)>>> d[:5]# An “interface” to matplotlib.axes.Axes.hist() methodn, bins, patches = plt.hist(x=d, bins=’auto’, color=’#0504aa’,alpha=0.7, rwidth=0.85)plt.grid(axis=’y’, alpha=0.75)plt.xlabel(‘Value’)plt.ylabel(‘Frequency’)plt.title(‘My First Histogram Ever’)plt.text(23, 45, r’$\mu=15, b=3$’)maxfreq = n.max()# Set a clean upper y-axis limit.plt.ylim(ymax=np.ceil(maxfreq / 10) * 10 if maxfreq % 10 else maxfreq + 10)

Explanation:

说明：

The pyplot.hist() in matplotlib lets you draw the histogram. It requires the array as the required input and you can specify the number of bins needed. A plot of a histogram uses its bin edges on the x-axis and the corresponding frequencies on the y-axis. In the chart above, passing bins=’auto’ chooses between two algorithms to estimate the “ideal” number of bins. At a high level, the goal of the algorithm is to choose a bin width that generates the most faithful representation of the data.

matplotlib中的pyplot.hist()允许您绘制直方图。 它需要将数组作为必需的输入，并且您可以指定所需的箱数。 直方图的图在x轴上使用其bin边缘，在y轴上使用相应的频率。 在上面的图表中，传递bins ='auto'在两种算法之间进行选择，以估计“理想”数目的bins。 在较高的层次上，该算法的目标是选择一个可生成最真实数据表示的bin宽度。

The output of source code: #Histogram Code mentioned above:

源代码的输出：上面提到的#Histogram代码：

3.散点图： (3. Scatter Plot:)

A scatter plot is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. If the points are coded (color/shape/size), one additional variable can be displayed. The data are displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.

散点图 是一种类型的曲线图，或者使用笛卡尔坐标来对一组数据的显示通常是两个变量的值的数学图中。 如果对点进行了编码(颜色/形状/大小)，则可以显示一个附加变量。 数据显示为点的集合，每个点的一个变量的值确定水平轴上的位置，而另一个变量的值确定水平轴上的位置。

A scatter plot can suggest various kinds of correlations between variables with a certain confidence interval. For example, weight and height, the weight would be on the y-axis, and height would be on the x-axis. Correlations may be positive (rising), negative (falling), or null (uncorrelated). If the pattern of dots slopes from lower left to upper right, it indicates a positive correlation between the variables being studied. If the pattern of dots slopes from the upper left to lower right, it indicates a negative correlation.

散点图可以建议具有一定置信区间的变量之间的各种相关性。 例如，重量和高度，重量将在y轴上，高度将在x轴上。 相关可能是正的(上升)，负的(下降)或无效(不相关)。 如果点的模式从左下到右上倾斜，则表明正在研究的变量之间存在正相关。 如果点的图案从左上角到右下角倾斜，则表示负相关。

Scatter plot Method format :

散点图方法格式：

matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, edgecolors=None, *, plotnonfinite=False, data=None, **kwargs)[source]

x, y : array_like, shape (n, )

x，y ：array_like，形状(n，)

The data positions.

数据位置。

s : scalar or array_like, shape (n, ), optional

s ：标量或类似array_，形状(n，)，可选

The marker size in points**2. Default is rcParams[‘lines.markersize’] ** 2.

标记大小(以磅为单位)** 2。 默认值为rcParams ['lines.markersize'] ** 2。

c : color, sequence, or sequence of color, optional

c ：颜色，序列或颜色序列，可选

For more detail about using the scatter plot method please refer the given link below:matplotlib.pyplot.scatter — Matplotlib 3.1.0 documentation

有关使用散点图方法的更多详细信息，请参考下面的给定链接： matplotlib.pyplot.scatter — Matplotlib 3.1.0文档

Edit descriptionmatplotlib.org

编辑descriptionmatplotlib.org

散点图示例： (Scatter Plot Example:)

#scatter plot lib example using matplotlibimport numpy as npimport matplotlib.pyplot as plt# Create dataN = 100x = np.random.rand(N)y = np.random.rand(N)colors = (0,100,255)area = np.pi*3# Plotplt.scatter(x, y, s=area, c=colors, alpha=0.5)plt.title(‘Scatter plot example using matplotlib’)plt.xlabel(‘x’)plt.ylabel(‘y’)plt.show()

Compile the code on your Jupyter notebook and you will see the outcome as given below:

在Jupyter笔记本上编译代码，您将看到如下结果：

通过实际数据集了解数据可视化： (Understanding Data Visualization Through Real Data Sets :)

We will be using the automobile data set, which we have downloaded from Kaggle, to understand data visualization using MatplotLib: Automobile Dataset

我们将使用从Kaggle下载的汽车数据集来了解使用MatplotLib的数据可视化： 汽车数据集

The dataset consists of various characters of an auto www.kaggle.com

数据集 由自动www.kaggle.com的各种字符组成

Always Remember:

总记得：

1. Download the Automobile.csv file from the above link从上面的链接下载Automobile.csv文件
2. Upload the file Jupyter into your working directory where your current code files lie.将文件Jupyter上载到当前代码文件所在的工作目录中。
3. Plotting Histogram: Using grouping data categorically :绘制直方图：归类使用分组数据：

We can have multiple histogram plots in the same plot. This helps you to compare the distribution of a continuous variable grouped by different categories.

我们可以在同一图中有多个直方图。 这可以帮助您比较按不同类别分组的连续变量的分布。

To understand it, we will be using Automobile.csv data sets:

为了理解它，我们将使用Automobile.csv数据集：

读取数据集： (Reading Data Sets:)

import pandas as pd#Reading data from the automobile #data sets using pandas read methoddf = pd.read_csv(‘Automobile.csv’)df.head()

When you compile this code you will see the below-given o/p as a series of data column-wise.

编译此代码时，您将看到以下给出的o / p列数据。

Let’s compare the distribution of car horsepower for a different type of car make in above-given data set of Automobile.csv

让我们比较一下车马力的分布在上面给出的数据不同类型的汽车品牌的设定Automobile.csv的

Write/Copy-paste below given code in your Jupyter notebook file:

在Jupyter笔记本文件中的给定代码下方写入/复制粘贴：

import matplotlib.pyplot as plt#is you don’t want to make a regular call on #plt.show use this line%matplotlib inlinex1 = df.loc[df.make==’alfa-romero’, ‘horsepower’]x2 = df.loc[df.make==’audi’, ‘horsepower’]x3 = df.loc[df.make==’bmw’, ‘horsepower’]x4 = df.loc[df.make==’toyota’, ‘horsepower’]x5 = df.loc[df.make==’volvo’, ‘horsepower’]kwargs = dict(alpha=0.9, bins=100)plt.hist(x1, **kwargs, color=’g’, label=’alfa-romero’)plt.hist(x2, **kwargs, color=’b’, label=’audi’)plt.hist(x3, **kwargs, color=’r’, label=’bmw’)plt.hist(x4, **kwargs, color=’y’, label=’toyota’)plt.hist(x5, **kwargs, color=’y’, label=’volvo’)plt.gca().set(title=’Horse power Variation for various make of a car’, ylabel=’Frequency’)#plt.xlim(50,200)plt.legend();

Below is a histogram Plot plotted against the given set of values using

以下是使用以下方法针对给定的一组值绘制的直方图

You can clearly make out that the larger concentration of horsepower lies between 110–120 hp.

您可以清楚地看出，更大的功率集中在110-120 hp之间。

散点图 ： (Scatter Plot :)

Let’s plot a data distribution using a scatter plot. Here we will try to see price distribution based on body_style of car.

让我们使用散点图绘制数据分布。 在这里，我们将尝试查看基于汽车的body_style的价格分布。

Copy /Paste the below-given code in your Jupyter notebook and compile it

在Jupyter笔记本中复制/粘贴下面给出的代码并进行编译

# Scatter Plotimport matplotlib.pyplot as plt%matplotlib inlineimport pandas as pddf = pd.read_csv(‘Automobile.csv’)bodystyle = df[‘body_style’] #fetching body type values rprice = df[‘price’] #fetching price for different body typeplt.scatter(bodystyle, price, edgecolors=’r’)plt.xlabel(‘body_style’, ‘make’)plt.ylabel(‘price (Rs)’)plt.title(‘Price variation based on car body type’)

Output:

输出：

观察： (Observation :)

You can see that there is a lot of data density around sedan type car and price mostly falls in the budget range of $10K to $15K. The second most used car body type comes out to be a hatchback. Wagon type mostly falls in the low-budget range.

您会看到，轿车类型的数据密度很高，价格大多在$ 10,000到$ 15K的预算范围内。 第二种最常用的车身类型是掀背车。 货车类型大多属于低预算范围。

下一步是什么 ： (What’s Next :)

There are more plots which we have not covered yet, like:

还有更多我们尚未涉及的地块，例如：

1. Violin plot

   小提琴情节

2. Stacked plot

   积积图

3. Stem Plot

   茎图

4. Line Plot

   线图

5. Box Plot

   箱形图

Which we will cover in part 2 of this series on Data Visualization. Also, we will cover

我们将在本系列的第2部分“数据可视化”中进行介绍。 另外，我们将介绍

“Data visualization using Seaborn package in detail “

“详细使用Seaborn包进行数据可视化”

何时使用哪种类型的数据可视化图/图表？ (When to Use What Type Of Data Visualization Plots/Charts?)

Leaving you all, with this wonderful pictorial representation of a data visualization graph type, which explains what type of graphs you can choose based on your data analysis requirements:

借助数据可视化图形类型的出色图形表示形式，您可以将所有内容交给所有人，它解释了根据数据分析要求可以选择哪种图形类型：

加起来： (Summing Up:)

It is absolutely recommended to add Data Science understanding for all software engineers who want to take advantage of all the amazing opportunities this field of data engineering is poised to offer. With data engineering augmented with AI/ML techniques, you can really grow fast and become an instrument of change for your organization or your own startup.

绝对建议所有想要利用此数据工程领域即将提供的惊人机会的软件工程师增加对Data Science的理解。 借助AI / ML技术增强的数据工程，您可以真正快速增长并成为组织或自己的创业公司的变革工具。

The Future will be all about data analysis, data prediction, product recommendations, and process automation, all these will require a lot of data engineers who can help organizations to make accurate, fast, and intelligent decisions regarding services and product offerings.

未来将只涉及数据分析，数据预测，产品推荐和流程自动化，所有这些都将需要大量数据工程师，他们可以帮助组织就服务和产品提供做出准确，快速和明智的决策。