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Introduction Big Data and Cloud PlatformIn the early years, telecommunications data storage was hampered by a variety of problems such as unwieldy numbers, a lack of computing power, prohibitive costs. But with the new technologies, the dimension of problems has changed.
The areas of use of Technology are:
· Cloud Platform enabling Data storage expenses to drop every day. (Azure, AWS)
· Computer processing power is increasing exponentially (Quantum Computing)
· Analytics software and tools are cheap and sometimes free (Knime, Python)
In earlier days, the data stores were expensive, and data was stored in siloed – separated and often incompatible – data stores. This was creating barriers to make use of an enormous volume and variety of information. Business Intelligence (BI) vendors like IBM, Oracle, SAS, Tibco, and QlikTech are breaking down these walls between data storage and this provides a lot of jobs for telecom data scientists.
Data Scientist roles in Telecom Sector 1. Network OptimizationWhen a network is down, underutilized, overtaxed, or nearing maximum capacity, the costs add up
In the past, telecom companies have handled this problem by putting caption data and developing tiered pricing models.
But now, using real-time and predictive analytics, companies analyze subscriber behavior and create individual network usage policies.
When the network goes down, every department (sales, marketing, customer service) can observe the effects, locate the customers affected, andimmediately implement efforts to address the issue.
When a customer suddenly abandons a shopping cart, customer service representatives can soothe concerns in a subsequent call, text, oremail.
Building360-degree profile of Network using CDRs, Alarms, Network Manuals, TemIP, etc. gives a better overview of the network health.
Not only does this make happy customers, but it also improves efficiencies and maximizes revenue streams.
Telecoms also have the option to combine their knowledge of network performance with internal data (e.g., customer usage or marketing initiatives) and external data (e.g., seasonal trends) to redirect resources (e.g., offers or capital investments) towards network hotspots.
2. Customer PersonalizationLike all the industries, Telecom has much more scope to personalize the services such as value-added services, data packs, apps to recommend based on following the behavioral patterns of customers. Sophisticated 360-degree profiles of customers assembled from all below help to build personalized recommendations for customers.
Customer Behaviourvoice, SMS, and data usage patterns
video choices
customer care history
social media activity
past purchase patterns
website visits, duration, browsing, and search patterns.
Customer Demographicsage, address, and gender
type and number of devices used.
service usage
geographic location
This allows telecom companies to offer personalized services or products at every step of the purchasing process. Businesses can tailor messages to appear on the right channels (e.g., mobile, web, call center, in-store), in the right areas, and in the right words and images.
Customer Segmentation, Sentiment analysis, Recommendation engines for more apt products for the customers are the illustrative areas where Data scientists can help for improvements.
3. Customer RetentionDue to customer dissatisfaction in any of the areas such as poor connection/network quality, poor services, high cost of services, call drops, competitors, less personalization, customer churn. This means they jump from network to network in search of bargains. This is one of the biggest challenges confronting a telecom company. It is far more costly to acquire new customers than to cater to existing ones.
To prevent churn, data scientists are employing both real-time and predictive analytics to:
Combinevariables (e.g., calls made, minutes used, number of texts sent, average bill amount, the average return per user i.e.ARPU) to predict the likelihoodof change.
Know when a customer visits a competitor’s website changes his/her SIM or swaps devices.
Use sentiment analysis of social media to detect changes in opinion.
Target specific customer segments with personalized promotions based on historical behavior.
React to retains customers as soon as the change is noted.
Predictive models, clustering would be the ways to predict the prospective churners.
Implemented Solution ApproachUsing big data and python, I have developed the solution to find the upcoming network failure before it takes place. The critical success factor defined were:
· Identify and prioritize the cells with call drop issues based on rules provided by the operator.
· Based on rules specified, provide relevant indicative information to network engineers that might have caused the issue in the particular cell.
· Provide a 360-degree view of network KPIs to the network engineer.
· Build a knowledge management database that can capture the actions taken to resolve the problem and
· Update the CRs as good and bad, based on effectiveness in resolving the network issue
As a huge data was getting created, the database used was Hadoop -Big Insights.
Data transformation scripts were in spark.
And the neural network was the ML technique used to find out the system parameters when historically alarms (the indication of network failure) in the system got generated.
This information was fed as a threshold and once in the real scenario the parameters start approaching the threshold, the internal alert for those cell sites get generated for the Network engineer to focus on as preventive analytics.
Once the network engineer identifies the problem and solves it, it gets documented in the knowledge repository for future reference.
And when exactly a similar situation occurs, network the engineer will not get notification of internal alert but also steps to solve which is build using knowledge repository.
ConclusionThe reduction in process time, dropped call rate, the volume of (transient) issues handled by engineer, mean time to solve the problem, cost, people and increase in Revenue, customers, customer satisfaction, efficiency, and productivity of network engineers are the main area of any industry which Data scientists would be of help.
Various data generation sources under Telecom sectors are booming areas for Data Scientists to innovate, explore, value add, and help the provider to provide data-driven AI/ML solutions by preventive analytics, process improvements, optimizations, predictive analytics.
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Top 6 Data Science Jobs In The Data
This data science career is doing very well on the market. Data science is making remarkable progress in many areas of technology, economy and commerce. It’s not an exaggeration. It is no surprise that data scientists will have many job opportunities.
It is true. Multiple projections show that the demand for data scientists will rise significantly in the next five-years. It is clear that demand is far greater than supply. Data science is a highly specialized field that requires a passion for math and analytical skills. This gap is perpetuated by the insufficient supply of these skills.
Every organization in the world is now data-driven. Data-driven organizations are the First Five: Google, Amazon, Facebook, Meta, Apple, Microsoft, and Facebook. They aren’t the only ones. Nearly every company in the market uses data-driven decision-making. The data sets can be customized quickly.
Amazon keeps meticulous records of all our choices and preferences in the world of shopping. It customizes the data to only send information that is relevant to the search terms of specific customers. Both the client and the company benefit from this process. This increases the company’s profit and helps the customer by acquiring goods at lower prices than they expected.
Data sets have a wider impact than just their positive effects. Data sets have positive effects on the health sphere by making people aware about critical health issues and other health-related items. It can also have an impact on agriculture, providing valuable information to farmers about efficient production and delivery of food.
It is evident that data scientists are needed around the globe, which makes their job prospects bright. Let’s take a look at some of the most exciting data science jobs available to data scientists who want to be effective in data management within organizations.
Top 6 Data Science Jobs in the Data-driven Industry 1. Data scientistsAverage Salary: US$100,000.
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2. Data architectsAverage Salary: US$95,000/annum
Roles and Responsibilities This employee is responsible for developing organizational data strategies that convert business requirements into technical requirements.
3. Data engineersAverage Salary: US$110,000 an Year
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4. Data analystsAverage Salary: US$70,000 an Year
Roles and Responsibilities. A data analyst must analyze real-time data using statistical techniques and tools in order to present reports to management. It is crucial to create and maintain a database and analyze and interpret current trends and patterns within those databases.
5. Data storytellerAverage Salary: US$60,000 an Year
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6. Database administratorsAverage Salary: US$80,000 an Year
Roles and Responsibilities of a database administrator: The database administrator must be proficient in database software to manage data effectively and keep it up-to date for data design and development. This employee will manage the database access and prevent loss and corruption.
These are only a few of the many data science jobs available to the world. In recent years, data science has been a thriving field in many industries around the globe. In this fast-paced world, data is increasingly valuable and there are many opportunities to fill data-centric roles within reputable organizations.
Pandas Cheat Sheet For Data Science In Python
What is Pandas Cheat Sheet?
Pandas library has many functions, but some of these are confusing for some people. We have here provided a helpful resource available called the Python Pandas Cheat Sheet. It explains the basics of Pandas in a simple and concise manner.
👉 Download the PDF of Cheat Sheet here
Whether you are a newbie or experienced with Pandas, this cheat sheet can serve as a useful reference guide. It covers a variety of topics, including working with Series and DataFrame data structures, selecting and ordering data, and applying functions to your data.
In summary, this Pandas Python Cheat Sheet is a good resource for anyone looking to learn more about using Python for Data Science. It is a handy reference tool. It can help you improve your data analysis skills and work more efficiently with Pandas.
Explaining important functions in Pandas:
To start working with pandas functions, you need to install and import pandas. There are two commands to do this:
Step 1) # Install PandasPip install pandas
Step 2) # Import PandasImport pandas as pd
Now, you can start working with Pandas functions. We will work to manipulate, analyze and clean the data. Here are some important functions of pandas.
Pandas Data StructuresAs we have already discussed that Pandas has two data structures called Series and DataFrames. Both are labeled arrays and can hold any data type. There is The only difference that Series is a one-dimensional array, and DataFrame is two-dimensional array.
1. SeriesIt is a one-dimensional labeled array. It can hold any data type.
s = pd.Series([2, -4, 6, 3, None], index=['A', 'B', 'C', 'D', 'E']) 2. DataFrameIt is a two-dimensional labeled array. It can hold any data type and different sizes of columns.
data = {'RollNo' : [101, 102, 75, 99], 'Name' : ['Mithlesh', 'Ram', 'Rudra', 'Mithlesh'], 'Course' : ['Nodejs', None, 'Nodejs', 'JavaScript'] } df = pd.DataFrame(data, columns=['RollNo', 'Name', 'Course']) df.head() Importing DataPandas have the ability to import or read various types of files in your Notebook.
Here are some examples given below.
# Import a CSV file pd pd.read_csv(filename) # Import a TSV file pd.read_table(filename) # Import a Excel file pd pd.read_excel(filename) # Import a SQL table/database pd.read_sql(query, connection_object) # Import a JSON file pd.read_json(json_string) # Import a HTML file pd.read_html(url) # From clipboard to read_table() pd.read_clipboard() # From dict pd.DataFrame(dict) SelectionYou can select elements by its location or index. You can select rows, columns, and distinct values using these techniques.
1. Series # Accessing one element from Series s['D'] # Accessing all elements between two given indices s['A':'C'] # Accessing all elements from starting till given index s[:'C'] # Accessing all elements from given index till end s['B':] 2. DataFrame # Accessing one column df df['Name'] # Accessing rows from after given row df[1:] # Accessing till before given row df[:1] # Accessing rows between two given rows df[1:2] Selecting by Boolean Indexing and Setting 1. By Position df.iloc[0, 1] df.iat[0, 1] 2. By Label df.loc[[0], ['Name']] 3. By Label/Position df.loc[2] # Both are same df.iloc[2] 4. Boolean Indexing# Use filter to adjust DataFrame
# Set index a of Series s to 6 s[‘D’] = 10 s.head()
Data CleaningFor data cleaning purposes, you can perform the following operations:
Rename columns using the rename() method.
Update values using the at[] or iat[] method to access and modify specific elements.
Create a copy of a Series or data frame using the copy() method.
Check for NULL values using the isnull() method, and drop them using the dropna() method.
Check for duplicate values using the duplicated() method. Drop them using the drop_duplicates() method.
Replace NULL values using the fill () method with a specified value.
Replace values using the replace() method.
Sort values using the sort_values() method.
Rank values using the rank() method.
# Renaming columns df.columns = ['a','b','c'] df.head() # Mass renaming of columns df = df.rename(columns={'RollNo': 'ID', 'Name': 'Student_Name'}) # Or use this edit in same DataFrame instead of in copy df.rename(columns={'RollNo': 'ID', 'Name': 'Student_Name'}, inplace=True) df.head() # Counting duplicates in a column df.duplicated(subset='Name') # Removing entire row that has duplicate in given column df.drop_duplicates(subset=['Name']) # You can choose which one keep - by default is first df.drop_duplicates(subset=['Name'], keep='last') # Checks for Null Values s.isnull() # Checks for non-Null Values - reverse of isnull() s.notnull() # Checks for Null Values df df.isnull() # Checks for non-Null Values - reverse of isnull() df.notnull() # Drops all rows that contain null values df.dropna() # Drops all columns that contain null values df.dropna(axis=1) # Replaces all null values with 'Guru99' df.fillna('Guru99') # Replaces all null values with the mean s.fillna(s.mean()) # Converts the datatype of the Series to float s.astype(float) # Replaces all values equal to 6 with 'Six' s.replace(6,'Six') # Replaces all 2 with 'Two' and 6 with 'Six' s.replace([2,6],['Two','Six']) # Drop from rows (axis=0) s.drop(['B', 'D']) # Drop from columns(axis=1) df.drop('Name', axis=1) # Sort by labels with axis df.sort_index() # Sort by values with axis df.sort_values(by='RollNo') # Ranking entries df.rank() # s1 is pointing to same Series as s s1 = s # s_copy of s, but not pointing same Series s_copy = s.copy() # df1 is pointing to same DataFrame as df df1 = s # df_copy of df, but not pointing same DataFrame df_copy = df.copy() Retrieving InformationYou can perform these operation to retrieve information:
Use shape attribute to get the number of rows and columns.
Use the head() or tail() method to obtain the first or last few rows as a sample.
Use the info(), describe(), or dtypes method to obtain information about the data type, count, mean, standard deviation, minimum, and maximum values.
Use the count(), min(), max(), sum(), mean(), and median() methods to obtain specific statistical information for values.
Use the loc[] method to obtain a row.
Use the groupby() method to apply the GROUP BY function to group similar values in a column of a DataFrame.
1. Basic information # Counting all elements in Series len(s) # Counting all elements in DataFrame len(df) # Prints number of rows and columns in dataframe df.shape # Prints first 10 rows by default, if no value set df.head(10) # Prints last 10 rows by default, if no value set df.tail(10) # For counting non-Null values column-wise df.count() # For range of index df df.index # For name of attributes/columns df.columns # Index, Data Type and Memory information df.info() # Datatypes of each column df.dtypes # Summary statistics for numerical columns df.describe() 2. Summary # For adding all values column-wise df.sum() # For min column-wise df.min() # For max column-wise df.max() # For mean value in number column df.mean() # For median value in number column df.median() # Count non-Null values s.count() # Count non-Null values df.count() # Return Series of given column df['Name'].tolist() # Name of columns df.columns.tolist() # Creating subset df[['Name', 'Course']] # Return number of values in each group df.groupby('Name').count() Applying Functions # Define function f = lambda x: x*5 # Apply this function on given Series - For each value s.apply(f) # Apply this function on given DataFrame - For each value df.apply(f) 1. Internal Data Alignment # NA values for indices that don't overlap s2 = pd.Series([8, -1, 4], index=['A', 'C', 'D']) s + s2 2. Arithmetic Operations with Fill Methods # Fill values that don't overlap s.add(s2, fill_value=0) 3. Filter, Sort and Group ByThese following functions can be used for filtering, sorting, and grouping by Series and DataFrame.
# Filter rows where column is greater than 100 # Filter rows where 70 < column < 101 # Sorts values in ascending order s.sort_values() # Sorts values in descending order s.sort_values(ascending=False) # Sorts values by RollNo in ascending order df.sort_values('RollNo') # Sorts values by RollNo in descending order df.sort_values('RollNo', ascending=False) Exporting DataPandas has the ability to export or write data in various formats. Here are some examples given below.
# Export as a CSV file df df.to_csv(filename) # Export as a Excel file df df.to_excel(filename) # Export as a SQL table df df.to_sql(table_name, connection_object) # Export as a JSON file df.to_json(filename) # Export as a HTML table df.to_html(filename) # Write to the clipboard df.to_clipboard() Conclusion:Pandas is open-source library in Python for working with data sets. Its ability to analyze, clean, explore, and manipulate data. It is an important tool for data scientists. Pandas is built on top of Numpy. It is used with other programs like Matplotlib and Scikit-learn. Pandas Cheat Sheet is a helpful resource for beginners and experienced users. It covers topics such as data structures, data selection, importing data, Boolean indexing, dropping values, sorting, and data cleaning. We have also prepared pandas cheat sheet pdf for article. Pandas is a library in Python and data science uses this library for working with pandas dataframes and series. We have discussed various pandas commands in this cheatsheet.
Colab of Cheat SheetMy Colab Exercise file for Pandas – Pandas Cheat Sheet – Python for Data Science.ipynb
Five Ways Data Science Has Evolved
According to Figure Eight’s Annual Data Science Report, 89% of data scientists love their activity, up from 67% in 2023. 49% of data scientists get reached in any event once every week for a new job. Data Scientists are essentially more inclined by almost 75% to trust that AI will be great on the planet when compared with 39% of morals experts. A ton has changed since the organization’s unique Data Science Report in 2023. Machine learning ventures are increasing and an ever-increasing number of data is required to drive them. Data Science and machine learning employments are LinkedIn’s more quickly developing occupations. Also, the web is making 2.5 quintillion bytes of information every day to power every last bit of it. Until a couple of years back, just a bunch of us had known about
Data science is more applied than any time in recent memory Difficulties Involved while dealing with Noisy Datasets Knowledge of applied science winsUnderstanding the inside operations of the black box has turned out to be less imperative, except if you are the maker of the black box. Less data scientists with genuinely deep learning of statistical strategies are kept in the lab making the secret elements that ideally get coordinated within tools. This is to some degree baffling for long time data experts with thorough statistical foundation and understanding, however, this way might be important to genuinely scale modeling endeavors with the volume of information, business questions, and complexities we currently should reply.
Transition from Data-Poor to Data-RichAs organizations progress from data poor enterprises to data-rich, wide experience and an intensive foundation in both data science and pure sciences will be required. With institutes hurrying to overcome any issues and adjusting educational programs to current industry request, the supply gap will steadily diminish. However, as individuals in their late 20s, 30s and even 40s hope to turn towards a profession in data science, they ought to essentially expand on critical, applied learning and get genuine hands-on understanding. One can’t turn into a data analyst with only one analytics track or online accreditation, one needs to augment a solid applied statistics program. Hands-on experience can go far in clearing the most troublesome ideas of data science.
Data Science is both art and science Data science and statistics are interconnectedAs the field develops, the job of data scientists will evolve. One of the definitions being bandied around is that data scientists are experts in statistics. In any case, it may not be the situation with the current part which has floated from the engineering field. We have frequently heard that data science can’t be more than statistics. Sean Owen, Director of Data Science at Cloudera noticed that statistics and numerical processing have been associated for a considerable length of time, and as in every aspect of computing, we generally ache for approaches to analyse somewhat more data. As indicated by John Tukey’s paper The Future of Data Analysis, statistics must wind up worried about the dealing with and processing of data, its size, and perception. In any case, today many individuals from different background, even economics, guarantee to be data scientists. Truth be told, the research additionally spread out a couple of characterized occurrences where a portion of the data science-related tasks could turn out to be totally automated, robotized selection and tuning. The tasks that will end up being the center range of abilities later on are, highlight building and model approval, comprehension of the area, machine learning.
Do You Know What Happened In The Data Science World?
Introduction
The world is becoming more complex than ever. The amount of information generated out there is too much and what we consume is not even equivalent to a dot in the Universe.
And as a Data Science company, Analytics Vidhya understands how difficult it can be to keep up with the news in the Data Science market. So to keep you in the loop and always updated with the current affairs in the data science industry, Analytics Vidhya brings to you its new initiative where we mention the top 5 news of the recent times with verified sources.
So here are the top 5 news that you might have missed-
1. Data of 500 million LinkedIn users BreachedOn 9th April 2023, Gadgets360 reports,
“LinkedIn Confirms Data Breach of 500 Million Subscribers, Personal Details Being Sold Online”
As per the news, information like email address, phone number, workplace information, full name, account IDs, links to their social media accounts, and gender details.
Reports say that the breached data is being sold on a hacker forum by an unknown user. The user has dumped data of over two million users as sample proof. The hacker is asking for a four-digit amount (in USD) in exchange for the breached data, potentially in the form of Bitcoins.
2. Streamlit Raises 35 billion in Series B fundingOn 7th April 2023, TechCrunch reports
“Streamlit nabs $35M Series B to expand machine learning platform”
This round of investment was led by Sequoia with aid from previous investors Gradient Ventures and GGV Capital.
Reports confirm that Streamlit will utilize this money to scale its team, expand the platform and bring its technology to leading enterprises.
Sonya Huang, the partner at Sequoia and Streamlit board member, said: “The field of data is changing rapidly. Static dashboards are no longer the best way for data scientists to communicate insights and democratize data access. Interactive, data-rich web apps are the future. We believe Streamlit has a unique opportunity to disrupt the $25 billion Business Intelligence market with its open-source and developer-first approach—ultimately, becoming a core piece of the data science and machine learning stack for years to come. We are thrilled to partner with this exceptional team as Streamlit continues to experience wide adoption within the data science community and in the Fortune 1000.”
3. Supreme Court of India launches Artificial Intelligence portal SUPACEOn 7th April 2023, India Today reports,
“Supreme Court embraces Artificial Intelligence, CJI Bobde says won’t let AI spill over to decision-making”.
The SC intends to make use of machine learning to deal with the vast amounts of data received at the time of filing of cases leading to the piling up of whole volumes of cases.
The speeches given by the esteemed judges at the virtual event suggest that SUPACE(Supreme Court Portal for Assistance in Courts Efficiency) will help them “address bottlenecks resulting in excessive delays” and “ease pendency” but will not take decision using the tool.
The prime court of India adopting AI is a clear sign that it does not want the piling up of huge volumes of cases to continue and provide swift justice to the Indian Citizens.
You can watch the virtual launch here-
4. Most Advanced Data Center Platform Launched in India by IntelOn 8th April 2023, Gadgets Now reports,
The new 3rd Gen Intel Xeon scalable processors form the foundation of this data center platform. Intel claims to have improved the workload by an average of 46% by delivering a significant performance increase.
The platform comes with new capabilities including Intel SGX for built-in security, and Intel Crypto Acceleration and Intel DL Boost for AI acceleration.
5. Microsoft Collaborates with Thales Alenia Space for Satellite Image ProcessingOn 8th April 2023, The Hindu reports,
“Microsoft partners with Aerospace firm for automated satellite image processing”
Microsoft and aerospace firm Thales Alenia Space (TAS) are partnering to embed the latter’s automated image processing solution to Microsoft’s Azure Orbital platform.
With TAS’s DeeperVision, all images downlinked by Earth observation satellites can be immediately and systematically analyzed as soon as they are produced, the aerospace company said in a release.
Tom Keane, CVP of Azure Global at Microsoft says,
“Processing space satellite imagery at cloud-scale changes the game for our customers who need these AI/ML data insights to quickly make informed decisions for mission success”
End NotesWe hope this initiative helps you keep up with the current happenings in the Data Science industry. This news brought in by Analytics Vidhya aims to add value to your data science journey by helping you understand the practical implication of the data science concepts.
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Top 10 Data Science Prerequisites You Should Know In 2023
Data science paves an enticing career path for students and existing professionals. Be it product development, improving customer retention, or mining through data to find new business opportunities, organizations are extensively relying on data scientists to sustain, grow, and stay one step ahead of the competition. This throws light on the growing demand for data scientists. If you, too, are aspiring to become a successful data scientist, you have landed at the right place for we will talk about the top 10 data science prerequisites you should know in 2023. Have a look!
Statistics
As a matter of fact, data science has a lot to do with data. In such a case, statistics turn out to be a blessing. This is for the sole reason that statistics help to dig deeper into data and gain valuable insights from them. The reality is – the more statistics you know, the more you will be able to analyze and quantify the uncertainty in a dataset.
Understanding analytical tools
Yet another important prerequisite for data science is to have a fair understanding of analytical tools. This is because a data scientist can extract valuable information from an organized data set via analytical tools. Some popular data analytical tools that you can get your hands on are – SAS, Hadoop, Spark, Hive, Pig, and R.
Programming
Data scientists are involved in procuring, cleaning, munging, and organizing data. For all of these tasks, programming comes in handy. Statistical programming languages such as R and Python serve the purpose here. If you want to excel as a data scientist, make sure that you are well-versed in Python and R.
Machine learning (ML)
Data scientists are entrusted with yet another important business task – identifying business problems and turning them into Machine Learning tasks. When you receive datasets, you are required to use your Machine Learning skills to feed the algorithms with data. ML will process these data in real time via data-driven models and efficient algorithms.
Apache Spark
Apache Spark is just the right computation framework you need when it comes to running complicated algorithms faster. With this framework, you can save time a lot of time while processing a big sea of data. In addition to that, it also helps Data Scientists handle large, unstructured, and complex data sets in the best possible manner.
Data Visualization
Yet another important prerequisite for data science that cannot go unnoticed is data visualization, a representation of data visually, through graphs and charts. As a data scientist, you should be able to represent data graphically, using charts, graphs, maps, etc. The extensive amount of data generated each day is the very reason why we require data visualization.
Communication skills
The fact that communication skill is one of the most important non-technical skill that one should possess, no matter what the job role is, goes without saying. Even in the case of data science, communication turns out to be an important prerequisite. This is because data scientists are required to clearly translate technical findings to the other non-technical teams like Sales, Operations or Marketing Departments. They should also be able to provide meaningful insights, hence enabling the business to make wiser decisions.
Excel
Excel is one tool that is extremely important to understand, manipulate, analyze and visualize data, hence a prerequisite for data science. With Excel, it is quite easy to proceed with manipulations and computations that have to be done on the data. Having sound Excel knowledge will definitely help you become a successful data scientist.
Teamwork
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