Proficiency level required:<\/strong> Foundational to intermediate<\/p>\n\n\n\nPython is the skill that most clearly separates entry-level data analyst roles (where Excel and SQL are often sufficient) from mid-level and senior roles (where Python is expected). In Mumbai’s 2025 hiring market, Python appears in roughly 70% of data analyst JDs \u2014 and that number rises to over 90% for roles with “senior” in the title or salary bands above \u20b912\u201315 LPA.<\/p>\n\n\n\n
The Python that matters for data analysts is not general-purpose Python. It is a specific subset centred on three libraries that appear together in virtually every data analyst Python JD.<\/p>\n\n\n\n
Pandas \u2014 for data manipulation and transformation<\/strong><\/p>\n\n\n\nPandas is the foundational library for working with tabular data in Python. If SQL is the language you use to query data out of a database, Pandas is the tool you use to clean, reshape, and transform that data once you have it in Python.<\/p>\n\n\n\n
import pandas as pd\n\n# Load data\ndf = pd.read_csv('mumbai_sales_data.csv')\n\n# Basic exploration\nprint(df.shape) # rows and columns\nprint(df.dtypes) # column data types\nprint(df.isnull().sum()) # count missing values per column\nprint(df.describe()) # summary statistics\n\n# Filter rows\nmumbai_orders = df[df['city'] == 'Mumbai']\n\n# Group and aggregate\ncity_revenue = df.groupby('city')['order_amount'].agg(['sum', 'mean', 'count'])\ncity_revenue.columns = ['total_revenue', 'avg_order', 'order_count']\ncity_revenue = city_revenue.sort_values('total_revenue', ascending=False)\n\nprint(city_revenue.head(10))\n<\/code><\/pre>\n\n\n\nWhat Mumbai JDs expect in Pandas:<\/strong> Data loading from CSV, Excel, and databases (pd.read_sql<\/code>), handling missing values (fillna<\/code>, dropna<\/code>, isnull<\/code>), data type conversion, filtering and selecting rows\/columns, groupby<\/code> with multiple aggregations, merging DataFrames (merge<\/code>, concat<\/code> \u2014 the Pandas equivalent of SQL JOINs), applying functions with apply<\/code>, and reshaping data with pivot_table<\/code> and melt<\/code>.<\/p>\n\n\n\nNumPy \u2014 for numerical operations<\/strong><\/p>\n\n\n\nNumPy provides the numerical computing foundation that Pandas is built on. For data analysts, direct NumPy usage is less common than Pandas, but understanding NumPy arrays, vectorised operations, and basic statistical functions (np.mean<\/code>, np.std<\/code>, np.percentile<\/code>) is expected at any level beyond entry.<\/p>\n\n\n\nMatplotlib and Seaborn \u2014 for visualisation in Python<\/strong><\/p>\n\n\n\nPython-based visualisation appears in data analyst JDs primarily for exploratory analysis and for automated reporting pipelines \u2014 contexts where the analyst needs to generate charts programmatically rather than through a BI tool.<\/p>\n\n\n\n
import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Distribution of order amounts\nplt.figure(figsize=(10, 6))\nsns.histplot(df['order_amount'], bins=50, kde=True)\nplt.title('Distribution of Order Amounts \u2014 Mumbai Market')\nplt.xlabel('Order Amount (\u20b9)')\nplt.ylabel('Frequency')\nplt.tight_layout()\nplt.savefig('order_distribution.png', dpi=150)\nplt.show()\n\n# Revenue by city \u2014 bar chart\ncity_revenue_reset = city_revenue.reset_index()\nplt.figure(figsize=(12, 6))\nsns.barplot(data=city_revenue_reset.head(10), x='city', y='total_revenue')\nplt.title('Top 10 Cities by Revenue')\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.show()\n<\/code><\/pre>\n\n\n\nWhat “job-ready” looks like for Python:<\/strong> You receive a raw CSV file with missing values, inconsistent data types, and columns that need to be transformed. You can clean it, explore it, answer specific analytical questions with Pandas, and produce charts that communicate the findings \u2014 without referencing documentation for the core operations.<\/p>\n\n\n\n
\n\n\n\nSkill 3: Power BI \u2014 The Mumbai Market Standard for BI<\/h2>\n\n\n\n
Frequency in Mumbai JDs:<\/strong> 68% Proficiency level required:<\/strong> Intermediate<\/p>\n\n\n\nPower BI’s dominance in Mumbai’s BI tool market has been covered in detail elsewhere (see our Power BI vs Tableau guide), but the specific skill depth expected in 2025 JDs is worth unpacking separately.<\/p>\n\n\n\n
The entry-level Power BI expectation \u2014 connecting to a data source, building basic charts, publishing a report \u2014 is no longer a differentiator. What Mumbai companies are now screening for is a meaningful step up from that baseline.<\/p>\n\n\n\n
The Power BI skills that appear most in Mumbai 2025 JDs:<\/strong><\/p>\n\n\n\nData modelling:<\/strong> Building a proper star schema in Power BI’s data model \u2014 fact tables connected to dimension tables, correct relationship directions, and understanding of cardinality. A Power BI report built on a flat single table is a red flag for experienced interviewers. A report built on a clean data model demonstrates genuine BI proficiency.<\/p>\n\n\n\nDAX (Data Analysis Expressions):<\/strong> The formula language of Power BI. The measures that appear most in Mumbai technical screens: CALCULATE<\/code> (the most important DAX function \u2014 modifies filter context), SUMX<\/code> and AVERAGEX<\/code> (row-by-row iteration), DIVIDE<\/code> (safe division with blank handling), SAMEPERIODLASTYEAR<\/code> and DATEADD<\/code> (time intelligence), RANKX<\/code> (ranking within a context), and IF<\/code>\/SWITCH<\/code> (conditional logic).<\/p>\n\n\n\n-- Total Revenue measure\nTotal Revenue = SUM(orders[order_amount])\n\n-- Revenue vs Last Year (time intelligence)\nRevenue LY = \nCALCULATE(\n [Total Revenue],\n SAMEPERIODLASTYEAR(dates[date])\n)\n\n-- YoY Growth %\nYoY Growth % = \nDIVIDE(\n [Total Revenue] - [Revenue LY],\n [Revenue LY],\n BLANK()\n)\n\n-- Running total\nRunning Total Revenue = \nCALCULATE(\n [Total Revenue],\n FILTER(\n ALLSELECTED(dates[date]),\n dates[date] <= MAX(dates[date])\n )\n)\n<\/code><\/pre>\n\n\n\nRow-Level Security (RLS):<\/strong> Setting up security roles so that different users see only the data they are authorised to see. This is a standard requirement in BFSI and enterprise deployments \u2014 and its presence in a JD signals a company that takes Power BI seriously as production infrastructure, not just a reporting tool.<\/p>\n\n\n\nPower Query (M):<\/strong> The data transformation layer of Power BI. While the GUI handles most transformations, understanding the M code that Power Query generates \u2014 and being able to write simple M expressions \u2014 is increasingly expected for mid-level roles.<\/p>\n\n\n\nWhat “job-ready” looks like:<\/strong> You receive a set of raw tables (a fact table and 3\u20134 dimension tables), build a correctly structured data model, write DAX measures for key business metrics (revenue, growth, running totals), and produce a clean, interactive dashboard that a business user can navigate without training.<\/p>\n\n\n\n
\n\n\n\nSkill 4: Excel \u2014 Still Essential, Differently Valued<\/h2>\n\n\n\n
Frequency in Mumbai JDs:<\/strong> 61% Proficiency level required:<\/strong> Advanced<\/p>\n\n\n\nExcel appears less frequently than SQL or Python in Mumbai data analyst JDs \u2014 but its appearance is more context-specific and its depth requirement is more often underestimated.<\/p>\n\n\n\n
The Excel that Mumbai companies are asking for in 2025 is not basic spreadsheet proficiency. It is advanced Excel<\/strong> \u2014 the tier that includes functions and features that most daily Excel users have never touched.<\/p>\n\n\n\nThe Excel skills that appear in Mumbai analyst JDs:<\/strong><\/p>\n\n\n\nVLOOKUP<\/code> and XLOOKUP<\/code> (lookup and matching), INDEX-MATCH<\/code> (more flexible than VLOOKUP, preferred in professional contexts), SUMIFS<\/code>, COUNTIFS<\/code>, AVERAGEIFS<\/code> (conditional aggregation), IFERROR<\/code> and IFNA<\/code> (error handling), array formulas and dynamic arrays (FILTER<\/code>, SORT<\/code>, UNIQUE<\/code>, SEQUENCE<\/code>), Pivot Tables with calculated fields and custom groupings, Power Query in Excel (for data transformation without VBA), and basic VBA macros for repetitive task automation.<\/p>\n\n\n\nWhere Excel still dominates over Python\/SQL in Mumbai:<\/strong><\/p>\n\n\n\nBFSI and banking operations teams where regulatory reporting and MIS (Management Information System) reports are produced in Excel format. Finance and accounts teams where the data lives in Excel and the output must be in Excel. Client-facing reporting in consulting and advisory roles where the client expects Excel deliverables. Any context where the analyst needs to share the data model alongside the analysis \u2014 a common requirement in Mumbai’s financial sector.<\/p>\n\n\n\n
What “job-ready” looks like:<\/strong> You receive a raw data extract in Excel, clean it using Power Query or advanced formulas, build a dynamic summary using Pivot Tables and calculated fields, and produce a formatted report that automatically updates when the underlying data is refreshed.<\/p>\n\n\n\n
\n\n\n\nSkill 5: Tableau \u2014 The Consulting and Enterprise Standard<\/h2>\n\n\n\n
Frequency in Mumbai JDs:<\/strong> 38% Proficiency level required:<\/strong> Intermediate<\/p>\n\n\n\nTableau appears in fewer Mumbai JDs than Power BI overall \u2014 but in specific segments of the market, it is the only tool that matters. Consulting firms, MNCs, and well-funded startups that have standardised on Tableau will specify it explicitly. A Power BI portfolio will not substitute.<\/p>\n\n\n\n
The Tableau skills that appear most in Mumbai JDs:<\/strong><\/p>\n\n\n\nCalculated fields and table calculations (Tableau’s equivalent of DAX measures), Level of Detail (LOD) expressions (FIXED<\/code>, INCLUDE<\/code>, EXCLUDE<\/code> \u2014 Tableau’s most powerful and most tested feature), dual-axis charts and combined chart types, parameters for user-driven filtering and scenario analysis, dashboard actions for interactive filtering between sheets, and Tableau Prep for data source preparation.<\/p>\n\n\n\nThe LOD expression specifically:<\/strong> This is the concept that most distinguishes intermediate from advanced Tableau users, and it appears in the technical screens of companies that take Tableau seriously. LOD expressions allow you to calculate at a different level of granularity than the current view \u2014 for example, calculating each customer’s first purchase date across the entire dataset while the view is showing order-level detail.<\/p>\n\n\n\n-- Customer's first order date (FIXED LOD)\n{ FIXED [Customer ID] : MIN([Order Date]) }\n\n-- Revenue as % of total (FIXED LOD)\nSUM([Revenue]) \/ { FIXED : SUM([Revenue]) }\n\n-- Number of customers who placed more than 3 orders (FIXED + filter)\n{ FIXED [Customer ID] : COUNT([Order ID]) } > 3\n<\/code><\/pre>\n\n\n\nWhat “job-ready” looks like:<\/strong> You can connect Tableau to a database, build a multi-sheet dashboard with at least one LOD expression, use parameters for dynamic user interaction, and publish a polished workbook to Tableau Public or Tableau Server.<\/p>\n\n\n\n
\n\n\n\nSkill 6: Statistics \u2014 The Quiet Differentiator<\/h2>\n\n\n\n
Frequency in Mumbai JDs:<\/strong> 44% Proficiency level required:<\/strong> Foundational to intermediate<\/p>\n\n\n\nStatistics appears less often than tools in Mumbai JDs \u2014 but when it does appear, it signals a role where the analyst is expected to go beyond reporting into genuine analysis. And in FinTech, banking, and e-commerce analytics roles specifically, statistical literacy is increasingly the skill that separates candidates who get offers from those who do not.<\/p>\n\n\n\n
The statistical concepts that appear most in Mumbai 2025 analyst JDs:<\/strong><\/p>\n\n\n\nDescriptive statistics:<\/strong> Mean, median, mode, standard deviation, variance, percentiles, skewness, kurtosis. These should be immediately applicable \u2014 given a dataset, you should be able to interpret distribution characteristics and explain what they mean for the business question.<\/p>\n\n\n\nProbability distributions:<\/strong> Understanding the normal distribution, binomial distribution, and their business applications. Knowing when a distribution is skewed and what that implies for using mean vs. median as a central tendency measure.<\/p>\n\n\n\nHypothesis testing:<\/strong> A\/B testing is the most common application of hypothesis testing in Mumbai’s e-commerce and product analytics roles. Understanding null and alternative hypotheses, p-values, statistical significance, and Type I\/Type II errors at a practical level \u2014 enough to design an A\/B test, interpret results, and explain the conclusion to a non-technical stakeholder \u2014 is what JDs mean when they list “statistical analysis.”<\/p>\n\n\n\nCorrelation and regression:<\/strong> Understanding the difference between correlation and causation (critical for analyst credibility), interpreting a correlation coefficient, and building and interpreting a simple linear regression. For roles in FinTech and banking, linear and logistic regression appear as explicit JD requirements.<\/p>\n\n\n\nCohort analysis and retention metrics:<\/strong> Widely used in Mumbai’s e-commerce and D2C sector. Understanding how to build a cohort retention table, calculate Month-1\/Month-3\/Month-6 retention rates, and interpret the resulting curves is a practical analytics skill that companies test directly.<\/p>\n\n\n\nWhat “job-ready” looks like:<\/strong> Given an A\/B test result with sample sizes, conversion rates, and a p-value, you can correctly interpret whether the result is statistically significant, whether the sample size was sufficient, and what conclusion the business should draw.<\/p>\n\n\n\n
\n\n\n\nSkill 7: Python for Automation and ML Basics \u2014 The Upskill Target<\/h2>\n\n\n\n
Frequency in Mumbai JDs (for ML-adjacent analyst roles):<\/strong> 52% Proficiency level required:<\/strong> Foundational<\/p>\n\n\n\nA growing segment of Mumbai data analyst JDs \u2014 particularly in FinTech, insurtech, and e-commerce \u2014 are beginning to include basic machine learning as a desired or required skill. This is not data scientist territory. It is the application of pre-built ML models to analyst tasks: customer segmentation, churn prediction, demand forecasting, and anomaly detection.<\/p>\n\n\n\n
The Python ML skills that appear most in these JDs:<\/strong><\/p>\n\n\n\nScikit-learn for classification and regression models (LogisticRegression<\/code>, RandomForestClassifier<\/code>, train_test_split<\/code>, accuracy_score<\/code>, confusion_matrix<\/code>), basic feature engineering and preprocessing (LabelEncoder<\/code>, StandardScaler<\/code>, OneHotEncoder<\/code>), and model evaluation metrics (accuracy, precision, recall, F1, AUC-ROC for classification; RMSE, MAE for regression).<\/p>\n\n\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import classification_report, confusion_matrix\nfrom sklearn.preprocessing import LabelEncoder\nimport pandas as pd\n\n# Load and prepare data\ndf = pd.read_csv('customer_churn.csv')\n\n# Basic feature engineering\nle = LabelEncoder()\ndf['city_encoded'] = le.fit_transform(df['city'])\n\n# Features and target\nX = df[['age', 'tenure_months', 'monthly_spend', 'city_encoded', 'num_transactions']]\ny = df['churned'] # 1 = churned, 0 = retained\n\n# Train-test split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Train model\nmodel = RandomForestClassifier(n_estimators=100, random_state=42)\nmodel.fit(X_train, y_train)\n\n# Evaluate\ny_pred = model.predict(X_test)\nprint(classification_report(y_test, y_pred))\nprint(confusion_matrix(y_test, y_pred))\n\n# Feature importance\nimportance_df = pd.DataFrame({\n 'feature': X.columns,\n 'importance': model.feature_importances_\n}).sort_values('importance', ascending=False)\nprint(importance_df)\n<\/code><\/pre>\n\n\n\nImportant clarification:<\/strong> This level of ML is not the expectation for most Mumbai analyst roles in 2025. It is the differentiator for mid-level roles that bridge analyst and data scientist functions. Freshers and career switchers should prioritise SQL, Python (Pandas), and Power BI before investing in ML basics.<\/p>\n\n\n\n
\n\n\n\nThe Mumbai Skills Priority Map: What to Learn in What Order<\/h2>\n\n\n\n
Given the frequency data and proficiency requirements above, here is the recommended learning sequence for each audience segment.<\/p>\n\n\n\n
For Freshers and Final-Year Students<\/h3>\n\n\n\n
Priority 1 \u2014 SQL (Weeks 1\u20134):<\/strong> This is the first interview filter at every company. Without SQL, you will not pass the technical screen. Master SELECT<\/code>, JOIN<\/code>, GROUP BY<\/code>, window functions, and CTEs before moving to anything else.<\/p>\n\n\n\nPriority 2 \u2014 Power BI (Weeks 5\u20138):<\/strong> The BI tool with the highest JD frequency and the lowest entry cost (free Desktop). Build 3\u20134 portfolio dashboards on real datasets. Learn basic DAX measures.<\/p>\n\n\n\nPriority 3 \u2014 Python \/ Pandas (Weeks 9\u201314):<\/strong> Start with data loading, cleaning, and groupby<\/code> aggregations. Progress to merging DataFrames and basic visualisation with Seaborn.<\/p>\n\n\n\nPriority 4 \u2014 Excel Advanced (Weeks 15\u201316):<\/strong> If your target roles are in BFSI or consulting, add Pivot Tables, INDEX-MATCH<\/code>, and Power Query in Excel.<\/p>\n\n\n\n
\n\n\n\nFor Career Switchers from Non-Tech Backgrounds<\/h3>\n\n\n\n
Month 1 \u2014 SQL:<\/strong> Non-negotiable starting point regardless of background.<\/p>\n\n\n\nMonth 2 \u2014 Excel Advanced + Power BI:<\/strong> If you have existing Excel proficiency, Power BI is the fastest path to a portfolio because the learning curve is gentler. The combination of advanced Excel and Power BI targets the widest range of Mumbai analyst entry-level JDs.<\/p>\n\n\n\nMonth 3 \u2014 Python \/ Pandas:<\/strong> Once SQL and Power BI are solid, Python adds the skill that enables mid-level role applications.<\/p>\n\n\n\n
\n\n\n\nFor Working Professionals Upskilling<\/h3>\n\n\n\n
Immediate gap-fill \u2014 SQL Window Functions:<\/strong> The most commonly tested advanced SQL skill in Mumbai’s 2025 technical screens. If you know basic SQL but not window functions, this is your highest-ROI learning investment.<\/p>\n\n\n\nAdd \u2014 Advanced DAX in Power BI:<\/strong> CALCULATE<\/code>, RANKX<\/code>, time intelligence functions. These separate mid-level from senior BI analyst profiles.<\/p>\n\n\n\nDifferentiate \u2014 Python for Automation:<\/strong> Building automated reporting pipelines using Python + Pandas + Matplotlib\/Plotly positions you for senior analyst roles that bridge analytics and engineering.<\/p>\n\n\n\n[Insert Visual: Mumbai Data Analyst Skills Priority Matrix \u2014 Frequency vs. Depth Required]<\/code><\/p>\n\n\n\n[Insert Table: Top 10 Companies Hiring Data Analysts in Mumbai 2025 and Their Required Tech Stack]<\/code><\/p>\n\n\n\n
\n\n\n\nThe Portfolio Is the Proof<\/h2>\n\n\n\n
Knowing these skills is necessary. Being able to demonstrate them is what gets you hired.<\/p>\n\n\n\n
For each skill on this list, your learning plan should include a portfolio output \u2014 a project, a GitHub repository, a published dashboard, or a documented analysis that shows a hiring manager what you can do, not just what you have studied.<\/p>\n\n\n\n
SQL portfolio:<\/strong> 5\u201310 analytical queries on a public dataset (Kaggle has excellent Indian e-commerce and finance datasets), published to GitHub with a README explaining the business questions answered.<\/p>\n\n\n\nPower BI portfolio:<\/strong> 2\u20133 dashboards published to Power BI Service or demonstrated via video walkthrough. Include one with a proper star schema data model and at least three non-trivial DAX measures.<\/p>\n\n\n\nPython portfolio:<\/strong> A Jupyter notebook that takes a raw, messy dataset through cleaning, exploration, and analytical conclusions. At least one visualisation that communicates a clear finding. Published to GitHub.<\/p>\n\n\n\nTableau portfolio (if targeting consulting):<\/strong> 3\u20135 workbooks on Tableau Public, with at least one using an LOD expression and one using parameters for interactivity.<\/p>\n\n\n\nThe portfolio is not a bonus. For analyst roles in Mumbai’s competitive market, it is increasingly the decisive factor when two candidates have similar educational backgrounds.<\/p>\n","protected":false},"excerpt":{"rendered":"
Every few months, a new tool or framework gets declared “the skill every data analyst must have.” The noise is loud, the lists are long, and for anyone trying to build a focused learning plan, the sheer volume of advice is more paralysing than helpful. This guide cuts through it. What follows is a ground-level […]<\/p>\n","protected":false},"author":1,"featured_media":818,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"ocean_post_layout":"","ocean_both_sidebars_style":"","ocean_both_sidebars_content_width":0,"ocean_both_sidebars_sidebars_width":0,"ocean_sidebar":"","ocean_second_sidebar":"","ocean_disable_margins":"enable","ocean_add_body_class":"","ocean_shortcode_before_top_bar":"","ocean_shortcode_after_top_bar":"","ocean_shortcode_before_header":"","ocean_shortcode_after_header":"","ocean_has_shortcode":"","ocean_shortcode_after_title":"","ocean_shortcode_before_footer_widgets":"","ocean_shortcode_after_footer_widgets":"","ocean_shortcode_before_footer_bottom":"","ocean_shortcode_after_footer_bottom":"","ocean_display_top_bar":"default","ocean_display_header":"default","ocean_header_style":"","ocean_center_header_left_menu":"","ocean_custom_header_template":"","ocean_custom_logo":0,"ocean_custom_retina_logo":0,"ocean_custom_logo_max_width":0,"ocean_custom_logo_tablet_max_width":0,"ocean_custom_logo_mobile_max_width":0,"ocean_custom_logo_max_height":0,"ocean_custom_logo_tablet_max_height":0,"ocean_custom_logo_mobile_max_height":0,"ocean_header_custom_menu":"","ocean_menu_typo_font_family":"","ocean_menu_typo_font_subset":"","ocean_menu_typo_font_size":0,"ocean_menu_typo_font_size_tablet":0,"ocean_menu_typo_font_size_mobile":0,"ocean_menu_typo_font_size_unit":"px","ocean_menu_typo_font_weight":"","ocean_menu_typo_font_weight_tablet":"","ocean_menu_typo_font_weight_mobile":"","ocean_menu_typo_transform":"","ocean_menu_typo_transform_tablet":"","ocean_menu_typo_transform_mobile":"","ocean_menu_typo_line_height":0,"ocean_menu_typo_line_height_tablet":0,"ocean_menu_typo_line_height_mobile":0,"ocean_menu_typo_line_height_unit":"","ocean_menu_typo_spacing":0,"ocean_menu_typo_spacing_tablet":0,"ocean_menu_typo_spacing_mobile":0,"ocean_menu_typo_spacing_unit":"","ocean_menu_link_color":"","ocean_menu_link_color_hover":"","ocean_menu_link_color_active":"","ocean_menu_link_background":"","ocean_menu_link_hover_background":"","ocean_menu_link_active_background":"","ocean_menu_social_links_bg":"","ocean_menu_social_hover_links_bg":"","ocean_menu_social_links_color":"","ocean_menu_social_hover_links_color":"","ocean_disable_title":"default","ocean_disable_heading":"default","ocean_post_title":"","ocean_post_subheading":"","ocean_post_title_style":"","ocean_post_title_background_color":"","ocean_post_title_background":0,"ocean_post_title_bg_image_position":"","ocean_post_title_bg_image_attachment":"","ocean_post_title_bg_image_repeat":"","ocean_post_title_bg_image_size":"","ocean_post_title_height":0,"ocean_post_title_bg_overlay":0.5,"ocean_post_title_bg_overlay_color":"","ocean_disable_breadcrumbs":"default","ocean_breadcrumbs_color":"","ocean_breadcrumbs_separator_color":"","ocean_breadcrumbs_links_color":"","ocean_breadcrumbs_links_hover_color":"","ocean_display_footer_widgets":"default","ocean_display_footer_bottom":"default","ocean_custom_footer_template":"","ocean_post_oembed":"","ocean_post_self_hosted_media":"","ocean_post_video_embed":"","ocean_link_format":"","ocean_link_format_target":"self","ocean_quote_format":"","ocean_quote_format_link":"post","ocean_gallery_link_images":"on","ocean_gallery_id":[],"footnotes":""},"categories":[3],"tags":[],"class_list":["post-794","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analytics","entry","has-media"],"acf":[],"_links":{"self":[{"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/posts\/794","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/comments?post=794"}],"version-history":[{"count":7,"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/posts\/794\/revisions"}],"predecessor-version":[{"id":899,"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/posts\/794\/revisions\/899"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/media\/818"}],"wp:attachment":[{"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/media?parent=794"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/categories?post=794"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techpaathshala.com\/blog\/wp-json\/wp\/v2\/tags?post=794"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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