5 Essential Tools to Start a Career in Data Science and Data Analytics | by Zoumana Keita | Jul, 2022
Learn these 5 tools to land your first job as Data Scientist or Data Analyst
Data Scientists’ job is to leverage large structured or unstructured datasets in order to draw meaningful information for better decision making. It combines both domain expertise, mathematical and statistical knowledge, data modeling, and result communication skills. However, they also need tools to give life to those concepts.
This article will build your understanding of those tools before highlighting their benefits.
There are a plethora of tools in the market whether open-source or paid license and upskilling with the relevant ones might help you optimize your portfolio and be operational for your next career in data.
The tools in the scope of this article are among the most used in the industry and have been divided into three main categories such as data analytics visualization, scripting/machine learning, and database management.
Data Analytics and Visualization Tools
Data visualization is a graphical representation of data. It is as important as any other aspect of a data science project. A clear and concise visualization can help communicate key information about data for better and quick decision-making because more than 65% of people are visual learners according to the ILS test statistics.
1 → Tableau
Tableau is no-code Business Intelligence software acquired by Salesforce in 2019. It provides an intuitive drag-and-drop interface for analytics and visualization. The non-technical aspect makes it stand out in the industry.
In addition, it is fast and provides the capability to interconnect data from multiple sources such as spreadsheets, SQL databases, etc. whether from the cloud or on-premise to create a single visualization. Tableau is the go-tool for the visualization of geospatial, and complex data. Also, it is compatible with popular programming languages such as Python, and R.
2 → Microsoft PowerBI
Similar to Tableau, PowerBI is also a Business Intelligence and Data Visualization tool, allowing the conversion of data from multiple sources into interactive business intelligence reports and also supports both Python and R.
But, what really differentiates them?
The main features differentiating it from Tableau are its inability to handle as much data as Tableau. In addition to that, it can connect to a limited number of data sources. For instance, Power BI does not work properly with NoSQL databases like MongoDB. However, it is affordable and can be suitable not only for medium and large companies but also for small ones.
Machine Learning and Scripting Tools
Every data scientist without exception needs to have programming skills either to create scripts for data processing and analysis or build machine learning models. Python and R are amongst the most popular programming languages for all Data Scientists.
3 → Python
The simplicity and flexibility offered by Python rapidly increased its adoption by Data Scientists. For instance, the following codes generate the same results Data Science and Analytics Tools for both Python and Java.
- For Python, we can type
pythonfrom the command line interpreter followed by theprintstatement as shown below.
# Step 1: open interpreter
python# Step 2: write the following expression to show the message
>>> print("Data Science and Analytics Tools")
- However, for java, we need to create a complete program and compile it to get the same result. This is because it does not have a command-line interpreter.
# Step 1: write this code in a file ShowMessage.javaclass ShowMessage {
public static void main(String[] args) {
System.out.println("Data Science and Analytics Tools");
}
}# Step 2: compile the file
javac ShowMessage.java# Step 3: execute the program to show the message
java ShowMessage
Besides being open-source, and with a large community, Python offers the following frameworks and libraries (not exhaustive) which are amongst the top ones for data analytics and machine learning. Data Scientists can:
- perform advanced numerical computing with
Numpy, providing compact and fast computations with multidimensional arrays. - leverage
Pandasfor data processing, cleaning, and analysis. It is widely used, and the most popular tool used by Data Scientists. - create from simple to more advanced data visualizations with
Matplotlib, andSeaborn, that can further be integrated into applications to generate dashboards. - implement almost all the machine learning and deep learning algorithms with
Scikit-learn,Pytorch, andKeras. - scrape data from the internet using
Beautifuland transform it into a suitable format and store to create a data store.
4 → R (Studio)
This programming language is created by statisticians, which makes it quite popular for statistical analysis and data visualization. It is widely used by data scientists and business analysts as well as in academia for research.
R incorporates tidyverse, a powerful set of tools for data science tasks (not exhaustive) such as:
- creating powerful data visualizations with
ggplot2. - implementing elegant pipelines for data modeling using
modelr. - performing data manipulation with
dplyr, a library that includes multiple handy functions to solve the most frequent tasks such as data filtering, selection, aggregation, etc. - loading data with
readrfor CSV and TSV data files,readxlfor Microsoft Excel data.
R does not only provide statistical and visualization features, but also machine learning capabilities with caret, a package with hundreds of algorithms.
Database Management
As Data Scientist, you must be able to retrieve structured or unstructured data from local or distance databases.
5 → SQL
Structured Query Language or SQL is a powerful language used by large, medium, and small data-driven businesses to explore and manipulate their data in order to extract relevant insights. This is because most of those companies use relational database systems such as PostgreSQL, MySQL, SQLite, etc, as we can observe from the following 2022 survey result made available by Stackoverflow.
This result undoubtedly makes SQL knowledge in high demand. It is even one of the most popular languages among Data Scientists/Machine Learning specialists, Data Analysts, Business Analysts, and Professional Developers overall.
Digging a little bit further on the survey, this graphic shows how widely used is SQL, compared to Python and R with respectively 54.64%, 43.51%, and 3.56%.
This finding is obviously not surprising, given the percentages of relational databases used by professional Developers. Also, one of the key take away from thos analysis is that businesses won’t get rid of SQL anytime soon.
The good news is that the human-readable aspect of SQL makes it one of the simplest languages to learn, and I came across this course on DataCamp that I believe might help you acquire the relevant skills to build your SQL portfolio.
Landing your first job as Data Scientist or Data Analyst can be quite intimidating. However, learning skills that meet the requirements of the job market can definitely help you build a strong portfolio to face those challenges. It is time to explore now, and get that first job you have been waiting for!
If you like reading my stories and wish to support my writing, consider becoming a Medium member to unlock unlimited access to stories on Medium.
Feel free to follow me on Medium, Twitter, or say Hi on LinkedIn. It is always a pleasure to discuss AI, ML, Data Science, NLP, and MLOps stuff!
Learn these 5 tools to land your first job as Data Scientist or Data Analyst
Data Scientists’ job is to leverage large structured or unstructured datasets in order to draw meaningful information for better decision making. It combines both domain expertise, mathematical and statistical knowledge, data modeling, and result communication skills. However, they also need tools to give life to those concepts.
This article will build your understanding of those tools before highlighting their benefits.
There are a plethora of tools in the market whether open-source or paid license and upskilling with the relevant ones might help you optimize your portfolio and be operational for your next career in data.
The tools in the scope of this article are among the most used in the industry and have been divided into three main categories such as data analytics visualization, scripting/machine learning, and database management.
Data Analytics and Visualization Tools
Data visualization is a graphical representation of data. It is as important as any other aspect of a data science project. A clear and concise visualization can help communicate key information about data for better and quick decision-making because more than 65% of people are visual learners according to the ILS test statistics.
1 → Tableau
Tableau is no-code Business Intelligence software acquired by Salesforce in 2019. It provides an intuitive drag-and-drop interface for analytics and visualization. The non-technical aspect makes it stand out in the industry.
In addition, it is fast and provides the capability to interconnect data from multiple sources such as spreadsheets, SQL databases, etc. whether from the cloud or on-premise to create a single visualization. Tableau is the go-tool for the visualization of geospatial, and complex data. Also, it is compatible with popular programming languages such as Python, and R.
2 → Microsoft PowerBI
Similar to Tableau, PowerBI is also a Business Intelligence and Data Visualization tool, allowing the conversion of data from multiple sources into interactive business intelligence reports and also supports both Python and R.
But, what really differentiates them?
The main features differentiating it from Tableau are its inability to handle as much data as Tableau. In addition to that, it can connect to a limited number of data sources. For instance, Power BI does not work properly with NoSQL databases like MongoDB. However, it is affordable and can be suitable not only for medium and large companies but also for small ones.
Machine Learning and Scripting Tools
Every data scientist without exception needs to have programming skills either to create scripts for data processing and analysis or build machine learning models. Python and R are amongst the most popular programming languages for all Data Scientists.
3 → Python
The simplicity and flexibility offered by Python rapidly increased its adoption by Data Scientists. For instance, the following codes generate the same results Data Science and Analytics Tools for both Python and Java.
- For Python, we can type
pythonfrom the command line interpreter followed by theprintstatement as shown below.
# Step 1: open interpreter
python# Step 2: write the following expression to show the message
>>> print("Data Science and Analytics Tools")
- However, for java, we need to create a complete program and compile it to get the same result. This is because it does not have a command-line interpreter.
# Step 1: write this code in a file ShowMessage.javaclass ShowMessage {
public static void main(String[] args) {
System.out.println("Data Science and Analytics Tools");
}
}# Step 2: compile the file
javac ShowMessage.java# Step 3: execute the program to show the message
java ShowMessage
Besides being open-source, and with a large community, Python offers the following frameworks and libraries (not exhaustive) which are amongst the top ones for data analytics and machine learning. Data Scientists can:
- perform advanced numerical computing with
Numpy, providing compact and fast computations with multidimensional arrays. - leverage
Pandasfor data processing, cleaning, and analysis. It is widely used, and the most popular tool used by Data Scientists. - create from simple to more advanced data visualizations with
Matplotlib, andSeaborn, that can further be integrated into applications to generate dashboards. - implement almost all the machine learning and deep learning algorithms with
Scikit-learn,Pytorch, andKeras. - scrape data from the internet using
Beautifuland transform it into a suitable format and store to create a data store.
4 → R (Studio)
This programming language is created by statisticians, which makes it quite popular for statistical analysis and data visualization. It is widely used by data scientists and business analysts as well as in academia for research.
R incorporates tidyverse, a powerful set of tools for data science tasks (not exhaustive) such as:
- creating powerful data visualizations with
ggplot2. - implementing elegant pipelines for data modeling using
modelr. - performing data manipulation with
dplyr, a library that includes multiple handy functions to solve the most frequent tasks such as data filtering, selection, aggregation, etc. - loading data with
readrfor CSV and TSV data files,readxlfor Microsoft Excel data.
R does not only provide statistical and visualization features, but also machine learning capabilities with caret, a package with hundreds of algorithms.
Database Management
As Data Scientist, you must be able to retrieve structured or unstructured data from local or distance databases.
5 → SQL
Structured Query Language or SQL is a powerful language used by large, medium, and small data-driven businesses to explore and manipulate their data in order to extract relevant insights. This is because most of those companies use relational database systems such as PostgreSQL, MySQL, SQLite, etc, as we can observe from the following 2022 survey result made available by Stackoverflow.
This result undoubtedly makes SQL knowledge in high demand. It is even one of the most popular languages among Data Scientists/Machine Learning specialists, Data Analysts, Business Analysts, and Professional Developers overall.
Digging a little bit further on the survey, this graphic shows how widely used is SQL, compared to Python and R with respectively 54.64%, 43.51%, and 3.56%.
This finding is obviously not surprising, given the percentages of relational databases used by professional Developers. Also, one of the key take away from thos analysis is that businesses won’t get rid of SQL anytime soon.
The good news is that the human-readable aspect of SQL makes it one of the simplest languages to learn, and I came across this course on DataCamp that I believe might help you acquire the relevant skills to build your SQL portfolio.
Landing your first job as Data Scientist or Data Analyst can be quite intimidating. However, learning skills that meet the requirements of the job market can definitely help you build a strong portfolio to face those challenges. It is time to explore now, and get that first job you have been waiting for!
If you like reading my stories and wish to support my writing, consider becoming a Medium member to unlock unlimited access to stories on Medium.
Feel free to follow me on Medium, Twitter, or say Hi on LinkedIn. It is always a pleasure to discuss AI, ML, Data Science, NLP, and MLOps stuff!
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