This project is inspired by Luke Barousse's Data Analyst Portfolio Project on YouTube.
The difference is that I adapted it to focus specifically on the Philippines, where I am from.
The dataset used in this project is credited to Luke Barousse and is available on his official site.
- 🔍 Perform EDA (Exploratory Data Analysis) on data roles worldwide —
providing insights such as 🌍 countries with the most job postings, 🏢 top hiring companies,
🏡 work-from-home possibilities, and more. - 💰 Analyze Salaries to compare pay across roles, regions, and companies.
- 🛠️ Explore Skills — identifying which skills 💵 pay better and 📈 are in demand.
- 📊 Provide an overview of skill trends, showing how demand evolves over time.
- 🏆 Discover the most optimal skill to learn for maximizing opportunities.
This section performs Exploratory Data Analysis (EDA) to summarize and visualize the data.
The plots are based on the sorting and counting of different columns in the dataset.
# Top 5 Data Roles, Countries, and Companies
df_da_roles = df["job_title_short"].value_counts().head(5).to_frame("count")
df_da_countries = df["job_country"].value_counts().head(5).to_frame("count")
df_da_companies = df["company_name"].value_counts().head(5).to_frame("count")
# Mapping columns for visualization
dict_column = {
'job_work_from_home': 'Work from Home Offered',
'job_no_degree_mention': 'Degree Requirement',
'job_health_insurance': 'Health Insurance Offered'
}
- 👨💻 Data Roles: Data Analysts hold the most positions, followed by Data Engineers and Data Scientists.
- 🌍 Job Locations: Most jobs are concentrated in English-speaking countries like the US, India, and the UK.
- 🏢 Top Company: Empreego has posted the most jobs by a wide margin.
- 🏠 Work Setup: Most jobs are performed at the office rather than remotely.
- 🎓 Degree Requirement: Only 30% of companies worldwide require a degree.
- 🩺 Benefits: Only 11% of companies offer health insurance.
This section performs Exploratory Data Analysis (EDA) to summarize and visualize the data.
The plots are based on the sorting and counting of different columns in the dataset.
The code bellow demonstrates the method of sorting the data relevant to this section's
goal.
# Top 5 Data Roles, Countries, and Companies
df_da_roles = df["job_title_short"].value_counts().head(5).to_frame("count")
df_da_countries = df["job_country"].value_counts().head(5).to_frame("count")
df_da_companies = df["company_name"].value_counts().head(5).to_frame("count")
# Mapping columns for visualization
dict_column = {
'job_work_from_home': 'Work from Home Offered',
'job_no_degree_mention': 'Degree Requirement',
'job_health_insurance': 'Health Insurance Offered'
}
- 👨💻 Data Roles: Data Analysts hold the most positions, followed by Data Engineers and Data Scientists.
- 🌍 Job Locations: Most jobs are concentrated in English-speaking countries like the US, India, and the UK.
- 🏢 Top Company: Empreego has posted the most jobs by a wide margin.
- 🏠 Work Setup: Most jobs are performed at the office rather than remotely.
- 🎓 Degree Requirement: Only 30% of companies worldwide require a degree.
- 🩺 Benefits: Only 11% of companies offer health insurance.
This section performs Salary Analysis to summarize and visualize the salary data in the Philippines.
The plots are based on the sorting and counting of different columns in the dataset.
The code below demonstrates how the data is filtered and processed for this analysis.
# Filter only Data Analyst jobs in PH + remove NaN salaries
df_ph_da = df[(df["job_title_short"] == "Data Analyst") & (df["job_country"] == "Philippines")].copy()
df_ph = df_ph_da.dropna(subset=["salary_year_avg"]).copy()
# Convert salary to PHP (assuming 1 USD = 56 PHP)
df_ph["salary_year_avg_php"] = df_ph["salary_year_avg"] * 56
# Explode skills for analysis
df_ph_skills = df_ph.explode("job_skills")
- 👨💻 Data Analyst Salaries: 50% of Data Analysts in the Philippines earn between ₱3M–₱6M per year.
- 📉 Limited Data: Machine Learning and Cloud Engineer roles show only vertical lines in the box charts, indicating very few data points are available.
- 🔹 Outliers: Only Data Engineers and Business Analysts have outliers, suggesting a relatively uniform salary trend across most roles.
- 💵 Highest-Paid Skills: BigQuery, C, Flow, Sheets, and Zoom are among the highest-paid skills in the Philippine setting, all earning over ₱6M per year.
- 📊 Most In-Demand Skills: SQL is the most in-demand skill among data roles in the Philippines, followed by Excel and Python.
This section performs Skills Analysis to summarize and visualize the top skills in demand in the Philippines.
The plots are based on the sorting and counting of different columns in the dataset.
The code below demonstrates how the data is filtered and processed for this analysis.
def plot_figure_1():
# Grouping the Data
df_skills_count = df_ph.groupby(["job_skills", "job_title_short"]).size()
df_skills_count = df_skills_count.reset_index(name="Skill_Count")
df_skills_count = df_skills_count.sort_values(by="Skill_Count", ascending=False)
# Top 3 Job Titles
df_job_titles = df_skills_count["job_title_short"].unique().tolist()
df_job_titles = sorted(df_job_titles[:3])
# Plotting Skills Count
fig, ax = plt.subplots(len(df_job_titles), 1)
sns.set_theme(style="ticks")
for i, job_title in enumerate(df_job_titles):
df_plot = df_skills_count[df_skills_count['job_title_short'] == job_title].head(5)[::-1]
sns.barplot(
data=df_plot,
x='Skill_Count',
y='job_skills',
ax=ax[i],
palette='dark:b_r'
)
ax[i].set_title(job_title)
ax[i].set_ylabel('')
ax[i].set_xlabel('Skill Count')
ax[i].invert_yaxis()
ax[i].set_xlim(0, 3000) # uniform scale
fig.suptitle('Top Skills in Job Postings (Philippines)', fontsize=15)
fig.tight_layout()
plt.show()
- 👩💼 Roles Requiring More Skills: Business Analysts, Data Analysts, and Data Engineers typically look for candidates with more skills.
- 🏆 Most Popular Skills: Excel is most popular among Business and Data Analysts, followed by SQL; Data Engineers favor SQL and Python.
- 📊 Roles with 5+ Skills Required: Business Analysts, Cloud Engineers, and Data Analysts often require five or more skills.
- 💻 Skill Preferences by Role: Excel remains popular for Business and Data Analysts, while Cloud Engineers favor AWS.
This section performs Skills Trend Analysis to summarize and visualize the top skills in demand for Data Analysts throughout the year in the Philippines.
The plots are based on the sorting and counting of different columns in the dataset.
The code below demonstrates how the data is filtered and processed for this analysis.
def plot_figure_1():
# Generate Pivot Table
df_pivot = df_ph_da_skills_exploded.pivot_table(
index="job_posted_month_no",
columns="job_skills",
aggfunc="size",
fill_value=0
)
# Reorder columns by total counts
df_pivot.loc["Total"] = df_pivot.sum()
df_pivot = df_pivot[df_pivot.loc['Total'].sort_values(ascending=False).index]
df_pivot = df_pivot.drop("Total")
# Convert month numbers → full month names
df_pivot.index = df_pivot.index.map(lambda x: calendar.month_name[x])
df_pivot = df_pivot.astype(int)
# Plot
sns.set_theme(style="whitegrid", rc={"figure.figsize": (14, 7)})
ax = sns.lineplot(
data=df_pivot.iloc[:, :5], # top 5 skills
dashes=False,
palette="tab10",
linewidth=2.2
)
# Titles only (no axis labels)
ax.set_title(
"Top 5 Skills Trends for Data Analyst Roles in the Philippines",
fontsize=16,
weight="bold",
pad=20
)
ax.set_ylabel("") # remove y-axis label
ax.set_xlabel("") # remove x-axis label
# Use month names as x-ticks
ax.set_xticks(range(len(df_pivot.index)))
ax.set_xticklabels(df_pivot.index, rotation=45, ha="right", fontsize=10)
ax.yaxis.set_major_formatter(mtick.StrMethodFormatter('{x:,.0f}'))
# Legend outside for clarity
ax.legend(
title="Skills",
fontsize=10,
title_fontsize=11,
loc="upper left",
bbox_to_anchor=(1.01, 1)
)
# Add grid styling
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()
- 📈 Peak Months: Most skills see the highest number of postings in January, with Excel being the most wanted skill, followed by SQL and Python.
- 📉 Low Months: Postings tend to dip in March and May, before recovering by June.
- ✨ Alteryx Spike: Alteryx postings peak particularly in March and October.
- 🏆 Top Skill Percentage: Alteryx has the highest overall percentages among the analyzed skills.
This section identifies the Most Optimal Skills for Data Analysts in the Philippines based on the dataset.
The analysis considers both skill demand and median salary, helping to determine which skills are most valuable to learn.
The plots are generated from sorting, aggregating, and visualizing relevant columns in the dataset.
# Filter only Data Analyst jobs in PH + remove NaN salaries
df_ph_da = df[(df["job_title_short"] == "Data Analyst") & (df["job_country"] == "Philippines")].copy()
df_ph = df_ph_da.dropna(subset=["salary_year_avg"]).copy()
# Convert salary to PHP (assuming 1 USD = 56 PHP)
df_ph["salary_year_avg_php"] = df_ph["salary_year_avg"] * 56
df_ph_skills = df_ph.explode("job_skills")
# Calculate Percentage on Job Postings that have skills
df_ph_da_skills = (
df_ph_skills
.groupby("job_skills")
.agg(
skill_count=("job_skills", "count"),
median_salary=("salary_year_avg_php", "median")
)
.sort_values(by="skill_count", ascending=False)
)
df_DA_job_count = len(df_ph_da) # total job postings
df_ph_da_skills['Skills Percentage'] = (df_ph_da_skills["skill_count"] / df_DA_job_count) * 100
# Keep only skills that appear in > 5 of postings
df_ph_da_skills_high_demand = df_ph_da_skills.head(10)
# Plot scatter
plt.figure(figsize=(10,6))
plt.scatter(
df_ph_da_skills_high_demand['Skills Percentage'],
df_ph_da_skills_high_demand['median_salary'],
s=80, alpha=0.7
)
plt.xlabel('Percent of Data Analyst Jobs')
plt.ylabel('Median Salary (₱PHP)')
plt.title('Most Optimal Skills for Data Analysts in the Philippines')
# Format y-axis as ₱xxxK
ax = plt.gca()
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, pos: f'₱{int(y/1000000)}M'))
# Add labels to points
texts = []
for i, txt in enumerate(df_ph_da_skills_high_demand.index):
texts.append(
plt.text(
df_ph_da_skills_high_demand['Skills Percentage'].iloc[i],
df_ph_da_skills_high_demand['median_salary'].iloc[i],
" " + txt
)
)
# Adjust text to avoid overlap and add arrows
adjust_text(texts,
arrowprops=dict(arrowstyle="->", color="black", lw=1),
force_points=0.5, force_text=0.5, expand_points=(1.2,1.2))
plt.show()
- 🏆 Top Skills: The most paid and in-demand skills are PowerPoint, R, Tableau, Python, and SQL.
- 💰 High-Paying Skills: Excel remains a highly paid skill, with median salaries around ₱5M per year.
- 📊 Optimal Balance: SQL is positioned farthest to the right on the scatter plot, indicating it is both highly paid and highly in-demand among Data Analysts in the Philippines.
Based on the analyses conducted on Data Analyst roles in the Philippines:
- 👨💻 Role Distribution: Data Analysts dominate the job market, followed by Data Engineers and Data Scientists. Jobs are mostly concentrated in English-speaking countries, with Empreego being the top hiring company.
- 💰 Salary Insights: Median salaries for Data Analysts range from ₱3M–₱6M per year, with skills like BigQuery, C, Flow, Sheets, and Zoom commanding the highest pay. SQL, Excel, and Python are the most in-demand skills in the Philippine market.
- 🛠️ Skills Analysis: Business Analysts, Data Analysts, and Data Engineers require candidates with multiple skills. Excel remains highly popular among analysts, while Cloud Engineers favor AWS.
- 📊 Skill Trends: Excel, SQL, and Python consistently appear as top skills throughout the year. Alteryx shows spikes in demand, particularly in March and October, indicating seasonal trends.
- 🏆 Optimal Skills: The most optimal skills for maximizing opportunities and pay are PowerPoint, R, Tableau, Python, and SQL, with SQL providing the best balance between high demand and high salary.
Overall, acquiring a combination of high-demand technical skills (SQL, Python, Tableau) along with business and presentation skills (PowerPoint, R) positions candidates for success in the Philippine Data Analyst market.
All scripts that generate two plots include a user choice loop, allowing the user to select which figure to display.
This makes the scripts interactive and easy to navigate.
# === User Choice Loop ===
print("> Which figure would you like to display (1 or 2)?: ")
print("> Figure 1: Salary Distribution for Data Analysts in the Philippines")
print("> Figure 2: Highest Paid Skills and Most In-Demand Skills")
while True:
choice = input("> ")
try:
if choice == "1":
plot_figure_1()
elif choice == "2":
plot_figure_2()
else:
print("Error: Please choose either 1 or 2")
continue
except Exception as e:
print(f"Error: {e}")
continue
again = input("Do you want to view the other chart? (Y/N): ")
if again.lower() in ["y", "yes"]:
if choice == "1":
plot_figure_2()
elif choice == "2":
plot_figure_1()
else:
break- Luke Barousse – for his insightful tutorials and for providing the original dataset that served as the foundation for this project.