Becoming a Data Analyst is Hard!
No, Not anymore.
Let me show you the "exact path" to becoming a Professional Data Analyst & get Certified.
- Thread -
No, Not anymore.
Let me show you the "exact path" to becoming a Professional Data Analyst & get Certified.
- Thread -
In this thread, I will lay out a plan to become a professional Data Analyst.
In addition, I have added courses and several resources that you will discover along the path.
Excited!!?
Let's get started!👇
In addition, I have added courses and several resources that you will discover along the path.
Excited!!?
Let's get started!👇
Here is my plan:
1. Gain foundational skills
2. Acquire data analysis skills
3. Learn industry-specific tools
4. Practice with real-world data
5. Network
6. Get certified
Let's start by learning the Foundational Skills👇
1. Gain foundational skills
2. Acquire data analysis skills
3. Learn industry-specific tools
4. Practice with real-world data
5. Network
6. Get certified
Let's start by learning the Foundational Skills👇
1. Gain foundational skills
Foundations Skill Include:
• Statistics
• Mathematics,
• Programming languages such as SQL, Python or R
Here are Statistical Concepts & Techniques you should master:
• Descriptive statistics:
Mean, median, mode, standard deviation, percentiles.
Foundations Skill Include:
• Statistics
• Mathematics,
• Programming languages such as SQL, Python or R
Here are Statistical Concepts & Techniques you should master:
• Descriptive statistics:
Mean, median, mode, standard deviation, percentiles.
• Probability theory:
Understanding of random variables, probability distributions, Bayes theorem
• Inferential statistics:
Hypothesis testing, estimation, correlation, regression analysis, ANOVA,
• Data visualization:
Creating visual representations of data using tools
Understanding of random variables, probability distributions, Bayes theorem
• Inferential statistics:
Hypothesis testing, estimation, correlation, regression analysis, ANOVA,
• Data visualization:
Creating visual representations of data using tools
• Exploratory Data Analysis (EDA):
Techniques for discovering patterns and relationships in data through visualization and statistical analysis.
• Sampling:
Understanding of different sampling techniques and how to choose the appropriate sample size
Techniques for discovering patterns and relationships in data through visualization and statistical analysis.
• Sampling:
Understanding of different sampling techniques and how to choose the appropriate sample size
• Modeling:
Knowledge of different statistical models, such as linear regression, logistic regression, decision trees
• Time series analysis:
Techniques for analyzing time-based data and making predictions based on historical trends.
Best Course to Learn Statistics 👇
Knowledge of different statistical models, such as linear regression, logistic regression, decision trees
• Time series analysis:
Techniques for analyzing time-based data and making predictions based on historical trends.
Best Course to Learn Statistics 👇
Best Course to Learn Statistics for Data Analysts
1. Coursera: "Statistics with R" by Duke University
2. edX: "Introduction to Statistics and Data Science" by MIT
3. DataCamp: "Statistics Fundamentals"
4. Udacity: "Intro to Statistics"
5. Udemy: "Statistics for Data Science"
1. Coursera: "Statistics with R" by Duke University
2. edX: "Introduction to Statistics and Data Science" by MIT
3. DataCamp: "Statistics Fundamentals"
4. Udacity: "Intro to Statistics"
5. Udemy: "Statistics for Data Science"
Programming Languages:
Next Foundational Skill you need to learn is programming languages.
This is where most people get stuck.
But remember this is a required skill today, to become a Data Analyst.
Programming Languages are 👇
Next Foundational Skill you need to learn is programming languages.
This is where most people get stuck.
But remember this is a required skill today, to become a Data Analyst.
Programming Languages are 👇
Programming Languages to Master as a Data Analyst:
• SQL:
Used for managing and retrieving data stored in relational databases.
• Python:
A popular general-purpose programming language used for data analysis, machine learning, and web development.
more 👇
• SQL:
Used for managing and retrieving data stored in relational databases.
• Python:
A popular general-purpose programming language used for data analysis, machine learning, and web development.
more 👇
• SAS (Statistical Analysis System):
A proprietary software suite used for advanced analytics, data management, and business intelligence.
• MATLAB:
A numerical computing environment, used for data analysis, modeling, and simulation.
Course you may want to check 👇
A proprietary software suite used for advanced analytics, data management, and business intelligence.
• MATLAB:
A numerical computing environment, used for data analysis, modeling, and simulation.
Course you may want to check 👇
Programming Couses for Data Analysts:
1. Coursera: "Programming for Data Science" by the University of Washington
2. DataCamp: "Intro to Python for Data Science"
3. Udemy: "SQL for Data Analysis"
4. Coursera: "Data Analysis with R" by Johns Hopkins University
1. Coursera: "Programming for Data Science" by the University of Washington
2. DataCamp: "Intro to Python for Data Science"
3. Udemy: "SQL for Data Analysis"
4. Coursera: "Data Analysis with R" by Johns Hopkins University
2. Acquire data analysis skills
Now, we are moving on to specific skills you need to acquire.
Skills include:
• Data cleaning
• Exploration
• Visualization techniques
What you need to know about these skills 👇
Now, we are moving on to specific skills you need to acquire.
Skills include:
• Data cleaning
• Exploration
• Visualization techniques
What you need to know about these skills 👇
• Data cleaning:
It is the process of identifying and correcting errors, and inaccuracies in a dataset to enhace the quality of data sets
Under the Data Cleaning you need to know how to perform various tasks.
Those tasks are 👇
It is the process of identifying and correcting errors, and inaccuracies in a dataset to enhace the quality of data sets
Under the Data Cleaning you need to know how to perform various tasks.
Those tasks are 👇
Data cleaning tasks are:
- Handling missing values
- Removing duplicates
- Handling outliers
- Correcting data errors
- Handling inconsistent data
- Data standardization
- Handling missing values
- Removing duplicates
- Handling outliers
- Correcting data errors
- Handling inconsistent data
- Data standardization
• Exploration
It is the process of analyzing and summarizing a dataset to gain a deeper understanding of its
• Structure
• Distribution
• Relationships among variables
It is a preliminary step and it helps data analysts to identify:
• Patterns
• Relationships
• Anomalies
It is the process of analyzing and summarizing a dataset to gain a deeper understanding of its
• Structure
• Distribution
• Relationships among variables
It is a preliminary step and it helps data analysts to identify:
• Patterns
• Relationships
• Anomalies
Data exploration tasks are:
- Univariate analysis
- Bivariate analysis
- Multivariate analysis
- Data visualization
- Dimensionality reduction
Remember, it is an iterative process that allows data analysts to gain a deeper understanding of the data, and develop hypotheses.
- Univariate analysis
- Bivariate analysis
- Multivariate analysis
- Data visualization
- Dimensionality reduction
Remember, it is an iterative process that allows data analysts to gain a deeper understanding of the data, and develop hypotheses.
• Visualization techniques
It is the process of creating visual representations of data to help communicate and explore insights and patterns in the data.
Techniques to create effective data visualizations, includes 👇
It is the process of creating visual representations of data to help communicate and explore insights and patterns in the data.
Techniques to create effective data visualizations, includes 👇
Effective Data Visualization Techniques:
- Bar charts
- Line charts
- Scatter plots
- Histograms
- Pie charts
- Box plots
- Heatmaps
- Maps
- Treemaps
Understanding when and where to use these techniques is important.
Want to know the courses to learn? 👇
- Bar charts
- Line charts
- Scatter plots
- Histograms
- Pie charts
- Box plots
- Heatmaps
- Maps
- Treemaps
Understanding when and where to use these techniques is important.
Want to know the courses to learn? 👇
Course to Learn Data Visualization:
1. Data Visualization with Tableau (Udemy): udemy.com
2. Data Visualization with ggplot2 (DataCamp):
datacamp.com
3. Data Visualization with Power BI (LinkedIn Learning):
linkedin.com
1. Data Visualization with Tableau (Udemy): udemy.com
2. Data Visualization with ggplot2 (DataCamp):
datacamp.com
3. Data Visualization with Power BI (LinkedIn Learning):
linkedin.com
âś…Quick Break:
If you are interest to follow a Roapmap, which is breakdown to
👉Daily tasks,
👉Free Resources included
👉Most Fequent Questions Included
+ a Comprehesive Python Course.
You can get it for $10 today, (Reg Price: $130)
goldsuite.gumroad.com
If you are interest to follow a Roapmap, which is breakdown to
👉Daily tasks,
👉Free Resources included
👉Most Fequent Questions Included
+ a Comprehesive Python Course.
You can get it for $10 today, (Reg Price: $130)
goldsuite.gumroad.com
3. Learn industry-specific tools
Now we are moving on to Learning Industry Specific Tools.
So far, I've included a variety of courses, if you complete those courses, you'll have almost covered this.
These tools differ by industry, task and specific jobs.
Tools are👇
Now we are moving on to Learning Industry Specific Tools.
So far, I've included a variety of courses, if you complete those courses, you'll have almost covered this.
These tools differ by industry, task and specific jobs.
Tools are👇
Data Analyst Tool Kit:
Microsoft Excel
Power BI
Tableau
Python
SPSS
SAS
SQL
R
By using the right tool for the job, data analysts can streamline their workflow and improve the quality of their analysis.
Microsoft Excel
Power BI
Tableau
Python
SPSS
SAS
SQL
R
By using the right tool for the job, data analysts can streamline their workflow and improve the quality of their analysis.
4. Practice with real-world data
Practicing Real World Data gives you real Hands on Experiences before handling a real task/jobs.
Let me show you how and where you can practice it. 👇
Practicing Real World Data gives you real Hands on Experiences before handling a real task/jobs.
Let me show you how and where you can practice it. 👇
Ways to Practice Real World Data:
- Kaggle:
A platform that provides a variety of real-world data sets and problems for data analysts.
- Internships
- Open data sets
These data sets can be found on:
• data[.]gov
• World Bank
• UCI Machine Learning Repository
- Kaggle:
A platform that provides a variety of real-world data sets and problems for data analysts.
- Internships
- Open data sets
These data sets can be found on:
• data[.]gov
• World Bank
• UCI Machine Learning Repository
- Personal projects:
Work on a personal Project and:
• Collect data from social media
• Online marketplaces
• Sports websites to analyze trends and patterns.
- Participate in hackathons
This will improve your skills, build portfolios, and gain experiences.
Work on a personal Project and:
• Collect data from social media
• Online marketplaces
• Sports websites to analyze trends and patterns.
- Participate in hackathons
This will improve your skills, build portfolios, and gain experiences.
5. Network
Moving on to networking.
This is something very few do, but if you want to succeed as a data analyst, even if you are on a job, you should build connection.
This will open your eyes to various opportunities also,
This will help you to stay on track and trends.
Moving on to networking.
This is something very few do, but if you want to succeed as a data analyst, even if you are on a job, you should build connection.
This will open your eyes to various opportunities also,
This will help you to stay on track and trends.
Tips for building your network as a data analyst:
• Attend industry events
• Join online communities
• Collaborate with others
• Participate in hackathons
• Network with potential employers
• Attend industry events
• Join online communities
• Collaborate with others
• Participate in hackathons
• Network with potential employers
6. Get Certified
I'm sure you know how important it is.
Let me list some benefits you can get:
• Credibility
• Career mobility
• Career advancement
• Professional recognition
• Helps to Stay up-to-date
So how can you get certified!
👇
I'm sure you know how important it is.
Let me list some benefits you can get:
• Credibility
• Career mobility
• Career advancement
• Professional recognition
• Helps to Stay up-to-date
So how can you get certified!
👇
Of course, you need to enroll in a certified Data Analyst Program.
There are several programs to get certified as a Data Analyst
Here are my favorite 5:
1. Microsoft Certified
shorturl.at
2. Google Data Analytics Professional Certificate
shorturl.at
There are several programs to get certified as a Data Analyst
Here are my favorite 5:
1. Microsoft Certified
shorturl.at
2. Google Data Analytics Professional Certificate
shorturl.at
3. IBM Data Analyst Professional Certificate:
shorturl.at
4. AWS Fundamentals Specialization:
shorturl.at
5. SAS Statistical Business Analyst Professional Certificate:
shorturl.at
Before Joining research and compare different programs.
shorturl.at
4. AWS Fundamentals Specialization:
shorturl.at
5. SAS Statistical Business Analyst Professional Certificate:
shorturl.at
Before Joining research and compare different programs.
That is it for this thread.
6 Steps to Become a Professional Data Analyst:
1. Gain foundational skills
2. Acquire data analysis skills
3. Learn industry-specific tools
4. Practice with real-world data
5. Network
6. Get certified
Bookmark this if you want to read later.
6 Steps to Become a Professional Data Analyst:
1. Gain foundational skills
2. Acquire data analysis skills
3. Learn industry-specific tools
4. Practice with real-world data
5. Network
6. Get certified
Bookmark this if you want to read later.
Hope you got some valuable lessons,
If you are interested in this type of content,
• Follow me @thegoldsuite
• RT the first tweet (below) to share and help others.
And start taking action now.
Thanks for reading :)
If you are interested in this type of content,
• Follow me @thegoldsuite
• RT the first tweet (below) to share and help others.
And start taking action now.
Thanks for reading :)
Here is the Simplest Data Analyst Path for Anyone
You will get:
âś…What to Learn Each Day
âś…Exact Lessons to become a DA
âś…Technologies you need to Learn
âś…Most Frequent Interview Ques
+ Full Python Course
You can get it for $10 today- Normal Price $130
goldsuite.gumroad.com
You will get:
âś…What to Learn Each Day
âś…Exact Lessons to become a DA
âś…Technologies you need to Learn
âś…Most Frequent Interview Ques
+ Full Python Course
You can get it for $10 today- Normal Price $130
goldsuite.gumroad.com
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